varying svr parameters: Topics by WorldWideScience.org

Sample records for varying svr parameters

Online Support Vector Regression with Varying Parameters for Time-Dependent Data

International Nuclear Information System (INIS)

Omitaomu, Olufemi A.; Jeong, Myong K.; Badiru, Adedeji B.

2011-01-01

Support vector regression (SVR) is a machine learning technique that continues to receive interest in several domains including manufacturing, engineering, and medicine. In order to extend its application to problems in which datasets arrive constantly and in which batch processing of the datasets is infeasible or expensive, an accurate online support vector regression (AOSVR) technique was proposed. The AOSVR technique efficiently updates a trained SVR function whenever a sample is added to or removed from the training set without retraining the entire training data. However, the AOSVR technique assumes that the new samples and the training samples are of the same characteristics; hence, the same value of SVR parameters is used for training and prediction. This assumption is not applicable to data samples that are inherently noisy and non-stationary such as sensor data. As a result, we propose Accurate On-line Support Vector Regression with Varying Parameters (AOSVR-VP) that uses varying SVR parameters rather than fixed SVR parameters, and hence accounts for the variability that may exist in the samples. To accomplish this objective, we also propose a generalized weight function to automatically update the weights of SVR parameters in on-line monitoring applications. The proposed function allows for lower and upper bounds for SVR parameters. We tested our proposed approach and compared results with the conventional AOSVR approach using two benchmark time series data and sensor data from nuclear power plant. The results show that using varying SVR parameters is more applicable to time dependent data.
On Input Vector Representation for the SVR model of Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

Determination and optimization of reactor core loading pattern is an important factor in nuclear power plant operation. The goal is to minimize the amount of enriched uranium (fresh fuel) and burnable absorbers placed in the core, while maintaining nuclear power plant operational and safety characteristics. The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. Recently, we proposed a new method for fast loading pattern evaluation based on general robust regression model relying on the state of the art research in the field of machine learning. We employed Support Vector Regression (SVR) technique. SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem. The preliminary tests revealed a good potential of the SVR method application for fast and accurate reactor core loading pattern evaluation. However, some aspects of model development are still unresolved. The main objective of the work reported in this paper was to conduct additional tests and analyses required for full clarification of the SVR applicability for loading pattern evaluation. We focused our attention on the parameters defining input vector, primarily its structure and complexity, and parameters defining kernel functions. All the tests were conducted on the NPP Krsko reactor core, using MCRAC code for the calculation of reactor core loading pattern critical parameters. The tested input vector structures did not influence the accuracy of the models suggesting that the initially tested input vector, consisted of the number of IFBAs and the k-inf at the beginning of the cycle, is adequate. The influence of kernel function specific parameters (σ for RBF kernel
Study on network traffic forecast model of SVR optimized by GAFSA

International Nuclear Information System (INIS)

Liu, Yuan; Wang, RuiXue

2016-01-01

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.
Analysis of process parameters in the laser deposition of YBa{sub 2}Cu{sub 3}O{sub 7} superconducting films by using SVR

Energy Technology Data Exchange (ETDEWEB)

Cai, C.Z., E-mail: caiczh@gmail.com [Department of Applied Physics, Chongqing University, Chongqing 401331 (China); Xiao, T.T. [Department of Applied Physics, Chongqing University, Chongqing 401331 (China); Science and Technology on Plasma Physics Laboratory, Research Center of Laser Fusion, CAEP, P.O. Box 919-988, Mianyang 621900 (China); Tang, J.L.; Huang, S.J. [Department of Applied Physics, Chongqing University, Chongqing 401331 (China)

2013-10-15

Highlights: • Proposed new ideas and strategies to improve energy storage density for SMES system. • One is to increase the effective current density in the superconducting coils. • Another is to optimize the configuration of the SMES coil. • A new conceive of energy compression is also proposed. -- Abstract: There are several process parameters in the growth of YBa{sub 2}Cu{sub 3}O{sub 7} superconducting films by using pulsed laser deposition (PLD). The relationship between the response and process parameters is highly nonlinear and quite complicated. It is very valuable to quantitatively estimate the response under different deposition parameters. In this study, according to an experimental data set on the superconducting transition temperature (T{sub c}) and relative resistance ratio (r{sub R}) of 17 samples of YBa{sub 2}Cu{sub 3}O{sub 7} films deposited under various parameters, the support vector regression (SVR) combined with particle swarm optimization (PSO), was proposed to predict the T{sub c} and r{sub R} for YBa{sub 2}Cu{sub 3}O{sub 7} films. The prediction performance of SVR was compared with that of multiple regression analysis (MRA) models. The results strongly support that the generalization ability of SVR model consistently surpasses that of MRA via leave-one-out cross validation (LOOCV). The mean absolute percentage errors for T{sub c} and r{sub R} are 0.37% and 1.51% respectively via LOOCV test of SVR. Sensitivity analysis discovered the most sensitive parameters affecting the T{sub c} and r{sub R}. This study suggests that the established SVR model can be used to accurately foresee the T{sub c} and r{sub R}. And it can be used to optimizing the deposition parameters in the development of YBa{sub 2}Cu{sub 3}O{sub 7} films via PLD.
Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

Directory of Open Access Journals (Sweden)

Xueqian Liu

2013-01-01

Full Text Available Maximum likelihood (ML algorithm is the most common and effective parameter estimation method. However, when dealing with small sample and low signal-to-noise ratio (SNR, threshold effects are resulted and estimation performance degrades greatly. It is proved that support vector machine (SVM is suitable for small sample. Consequently, we employ the linear relationship between least squares support vector regression (LS-SVR’s inputs and outputs and regard LS-SVR process as a time-varying linear filter to increase input SNR of received signals and decrease the threshold value of mean square error (MSE curve. Furthermore, it is verified that by taking single-tone sinusoidal frequency estimation, for example, and integrating data analysis and experimental validation, if LS-SVR’s parameters are set appropriately, not only can the LS-SVR process ensure the single-tone sinusoid and additive white Gaussian noise (AWGN channel characteristics of original signals well, but it can also improves the frequency estimation performance. During experimental simulations, LS-SVR process is applied to two common and representative single-tone sinusoidal ML frequency estimation algorithms, the DFT-based frequency-domain periodogram (FDP and phase-based Kay ones. And the threshold values of their MSE curves are decreased by 0.3 dB and 1.2 dB, respectively, which obviously exhibit the advantage of the proposed algorithm.
Pair- ${v}$ -SVR: A Novel and Efficient Pairing nu-Support Vector Regression Algorithm.

Science.gov (United States)

Hao, Pei-Yi

This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair--SVR has additional advantage of using parameter for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair--SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair--SVR in both training/prediction speed and generalization ability.This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory
The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

Directory of Open Access Journals (Sweden)

Mohammed Amroune

2017-10-01

Full Text Available This paper presents the application of support vector regression (SVR and adaptive neuro-fuzzy inference system (ANFIS models that are amalgamated with synchronized phasor measurements for on-line voltage stability assessment. As the performance of SVR model extremely depends on the good selection of its parameters, the recently developed ant lion optimizer (ALO is adapted to seek for the SVR’s optimal parameters. In particular, the input vector of ALO-SVR and ANFIS soft computing models is provided in the form of voltage magnitudes provided by the phasor measurement units (PMUs. In order to investigate the effectiveness of ALO-SVR and ANFIS models towards performing the on-line voltage stability assessment, in-depth analyses on the results have been carried out on the IEEE 30-bus and IEEE 118-bus test systems considering different topologies and operating conditions. Two statistical performance criteria of root mean square error (RMSE and correlation coefficient (R were considered as metrics to further assess both of the modeling performances in contrast with the power flow equations. The results have demonstrated that the ALO-SVR model is able to predict the voltage stability margin with greater accuracy compared to the ANFIS model.
Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Li-Ling Peng

2016-03-01

Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD method and quantum particle swarm optimization algorithm (QPSO for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia market and the New York Independent System Operator (NYISO, New York, USA are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

Science.gov (United States)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.
Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

International Nuclear Information System (INIS)

Hong, Wei-Chiang

2011-01-01

Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.
Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

Science.gov (United States)

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

Science.gov (United States)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.
Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting

International Nuclear Information System (INIS)

Zhang, Wen Yu; Hong, Wei-Chiang; Dong, Yucheng; Tsai, Gary; Sung, Jing-Tian; Fan, Guo-feng

2012-01-01

The electric load forecasting is complicated, and it sometimes reveals cyclic changes due to cyclic economic activities or climate seasonal nature, such as hourly peak in a working day, weekly peak in a business week, and monthly peak in a demand planned year. Hybridization of support vector regression (SVR) with chaotic sequence and evolutionary algorithms has successfully been applied to improve forecasting accuracy, and to effectively avoid trapping in a local optimum. However, it has not been widely explored to employ SVR-based model to deal with cyclic electric load forecasting. This paper will firstly investigate the potentiality of a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), with an SVR model to improve load forecasting accurate performance. In which, the proposed CGASA employs internal randomness of chaotic iterations to overcome premature local optimum. Secondly, the seasonal mechanism will then be applied to well adjust the cyclic load tendency. Finally, a numerical example from an existed reference is employed to compare the forecasting performance of the proposed SSVRCGASA model. The forecasting results show that the SSVRCGASA model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. -- Highlights: ► Hybridizing the seasonal adjustment mechanism into an SVR model. ► Employing chaotic sequence to improve the premature convergence of genetic algorithm and simulated annealing algorithm. ► Successfully providing significant accurate monthly load demand forecasting.
Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM

Science.gov (United States)

Mukherjee, Amritendu; Ramachandran, Parthasarathy

2018-03-01

Prediction of Ground Water Level (GWL) is extremely important for sustainable use and management of ground water resource. The motivations for this work is to understand the relationship between Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water change (ΔTWS) data and GWL, so that ΔTWS could be used as a proxy measurement for GWL. In our study, we have selected five observation wells from different geographic regions in India. The datasets are unevenly spaced time series data which restricts us from applying standard time series methodologies and therefore in order to model and predict GWL with the help of ΔTWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). Comparative performances of LRM, SVR and ANN have been evaluated with the help of correlation coefficient (ρ) and Root Mean Square Error (RMSE) between the actual and fitted (for training dataset) or predicted (for test dataset) values of GWL. It has been observed in our study that ΔTWS is highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of ΔTWS varies from 36.48% to 74.28% (0.3648 ⩽R2 ⩽ 0.7428) . We have found that for the model GWL ∼ Δ TWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE. It also has been found in our study that with the inclusion of meteorological variables along with ΔTWS as input parameters to model GWL, the performance of SVR improves and it performs better than ANN. These results imply that for modelling irregular time series GWL data, ΔTWS could be very useful.
PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

Science.gov (United States)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and
The application of SVR model in the improvement of QbD: a case study of the extraction of podophyllotoxin.

Science.gov (United States)

Zhai, Chun-Hui; Xuan, Jian-Bang; Fan, Hai-Liu; Zhao, Teng-Fei; Jiang, Jian-Lan

2018-05-03

In order to make a further optimization of process design via increasing the stability of design space, we brought in the model of Support Vector Regression (SVR). In this work, the extraction of podophyllotoxin was researched as a case study based on Quality by Design (QbD). We compared the fitting effect of SVR and the most used quadratic polynomial model (QPM) in QbD, and an analysis was made between the two design spaces obtained by SVR and QPM. As a result, the SVR stayed ahead of QPM in prediction accuracy, the stability of model and the generalization ability. The introduction of SVR into QbD made the extraction process of podophyllotoxin well designed and easier to control. The better fitting effect of SVR improved the application effect of QbD and the universal applicability of SVR, especially for non-linear, complicated and weak-regularity problems, widened the application field of QbD.
Improvement of Surface Temperature Prediction Using SVR with MOGREPS Data for Short and Medium range over South Korea

Science.gov (United States)

Lim, S. J.; Choi, R. K.; Ahn, K. D.; Ha, J. C.; Cho, C. H.

2014-12-01

As the Korea Meteorology Administration (KMA) has operated Met Office Global and Regional Ensemble Prediction System (MOGREPS) with introduction of Unified Model (UM), many attempts have been made to improve predictability in temperature forecast in last years. In this study, post-processing method of MOGREPS for surface temperature prediction is developed with machine learning over 52 locations in South Korea. Past 60-day lag time was used as a training phase of Support Vector Regression (SVR) method for surface temperature forecast model. The selected inputs for SVR are followings: date and surface temperatures from Numerical Weather prediction (NWP), such as GDAPS, individual 24 ensemble members, mean and median of ensemble members for every 3hours for 12 days.To verify the reliability of SVR-based ensemble prediction (SVR-EP), 93 days are used (from March 1 to May 31, 2014). The result yielded improvement of SVR-EP by RMSE value of 16 % throughout entire prediction period against conventional ensemble prediction (EP). In particular, short range predictability of SVR-EP resulted in 18.7% better RMSE for 1~3 day forecast. The mean temperature bias between SVR-EP and EP at all test locations showed around 0.36°C and 1.36°C, respectively. SVR-EP is currently extending for more vigorous sensitivity test, such as increasing training phase and optimizing machine learning model.
Evaluation of svr: a wireless sensor network routing protocol

International Nuclear Information System (INIS)

Baloch, J.; Khanzada, T.J.S.

2014-01-01

The advancement in technology has made it possible to create small in size, low cost sensor nodes. However, the small size and low cost of such nodes comesat at price that is, reduced processing power, low memory and significantly small battery energy storage. WSNs (Wireless Sensor Networks) are inherently ad hoc in nature and are assumed to work in the toughest terrain. The network lifetime plays a pivotal role in a wireless sensor network. A long network lifetime, could be achieved by either making significant changes in these low cost devices, which is not a feasible solution or by improving the means of communication throughout the network. The communication in such networks could be improved by employing energy efficient routing protocols, to route the data throughout the network. In this paper the SVR (Spatial Vector Routing) protocol is compared against the most common WSN routing protocols, and from the results it could be inferred that the SVR protocol out performs its counterparts. The protocol provides an energy efficient means of communication in the network. (author)
Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles

Directory of Open Access Journals (Sweden)

Yiqing Huang

2016-11-01

Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.
Modification of textural and acidic properties of -SVR zeolite by desilication

Czech Academy of Sciences Publication Activity Database

Kubů, Martin; Opanasenko, Maksym; Shamzhy, Mariya

2014-01-01

Roč. 227, MAY 2014 (2014), s. 26-32 ISSN 0920-5861 R&D Projects: GA ČR GP13-17593P Institutional support: RVO:61388955 Keywords : -SVR zeolite * desilication * hierarchical materials Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.893, year: 2014

Edge Modeling by Two Blur Parameters in Varying Contrasts.

Science.gov (United States)

Seo, Suyoung

2018-06-01

This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.
Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting

Directory of Open Access Journals (Sweden)

Cheng-Wen Lee

2016-10-01

Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.
Real time flaw detection and characterization in tube through partial least squares and SVR: Application to eddy current testing

Science.gov (United States)

Ahmed, Shamim; Miorelli, Roberto; Calmon, Pierre; Anselmi, Nicola; Salucci, Marco

2018-04-01

This paper describes Learning-By-Examples (LBE) technique for performing quasi real time flaw localization and characterization within a conductive tube based on Eddy Current Testing (ECT) signals. Within the framework of LBE, the combination of full-factorial (i.e., GRID) sampling and Partial Least Squares (PLS) feature extraction (i.e., GRID-PLS) techniques are applied for generating a suitable training set in offine phase. Support Vector Regression (SVR) is utilized for model development and inversion during offine and online phases, respectively. The performance and robustness of the proposed GIRD-PLS/SVR strategy on noisy test set is evaluated and compared with standard GRID/SVR approach.
Improved SVR Model for Multi-Layer Buildup Factor Calculation

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2006-01-01

The accuracy of point kernel method applied in gamma ray dose rate calculations in shielding design and radiation safety analysis is limited by the accuracy of buildup factors used in calculations. Although buildup factors for single-layer shields are well defined and understood, buildup factors for stratified shields represent a complex physical problem that is hard to express in mathematical terms. The traditional approach for expressing buildup factors of multi-layer shields is through semi-empirical formulas obtained by fitting the results of transport theory or Monte Carlo calculations. Such an approach requires an ad-hoc definition of the fitting function and often results with numerous and usually inadequately explained and defined correction factors added to the final empirical formula. Even more, finally obtained formulas are generally limited to a small number of predefined combinations of materials within relatively small range of gamma ray energies and shield thicknesses. Recently, a new approach has been suggested by the authors involving one of machine learning techniques called Support Vector Machines, i.e., Support Vector Regression (SVR). Preliminary investigations performed for double-layer shields revealed great potential of the method, but also pointed out some drawbacks of the developed model, mostly related to the selection of one of the parameters describing the problem (material atomic number), and the method in which the model was designed to evolve during the learning process. It is the aim of this paper to introduce a new parameter (single material buildup factor) that is to replace the existing material atomic number as an input parameter. The comparison of two models generated by different input parameters has been performed. The second goal is to improve the evolution process of learning, i.e., the experimental computational procedure that provides a framework for automated construction of complex regression models of predefined
A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-25

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Analisis Perbandingan Teknik Support Vector Regression (SVR) Dan Decision Tree C4.5 Dalam Data Mining

OpenAIRE

Astuti, Yuniar Andi

2011-01-01

This study examines techniques Support Vector Regression and Decision Tree C4.5 has been used in studies in various fields, in order to know the advantages and disadvantages of both techniques that appear in Data Mining. From the ten studies that use both techniques, the results of the analysis showed that the accuracy of the SVR technique for 59,64% and C4.5 for 76,97% So in this study obtained a statement that C4.5 is better than SVR 097038020
Performance of a solar chimney by varying design parameters

CSIR Research Space (South Africa)

Kumirai, T

2015-08-01

Full Text Available the design of solar chimneys to ensure optimal performance. The purpose of this chapter is to discuss the performance of an example solar chimney by varying the design parameters and examining their effects on the interior ventilation performance... chimney by varying design parameters Tichaona Kumirai, Researcher, Built Environment CSIR Jan-Hendrik Grobler, DPSS CSIR Dr D.C.U. Conradie, Senior researcher, Built Environment CSIR 1 Introduction Trombe walls and solar chimneys are not widely...
Mutations in circularly permuted GTPase family genes AtNOA1/RIF1/SVR10 and BPG2 suppress var2-mediated leaf variegation in Arabidopsis thaliana.

Science.gov (United States)

Qi, Yafei; Zhao, Jun; An, Rui; Zhang, Juan; Liang, Shuang; Shao, Jingxia; Liu, Xiayan; An, Lijun; Yu, Fei

2016-03-01

Leaf variegation mutants constitute a unique group of chloroplast development mutants and are ideal genetic materials to dissect the regulation of chloroplast development. We have utilized the Arabidopsis yellow variegated (var2) mutant and genetic suppressor analysis to probe the mechanisms of chloroplast development. Here we report the isolation of a new var2 suppressor locus SUPPRESSOR OF VARIEGATION (SVR10). Genetic mapping and molecular complementation indicated that SVR10 encodes a circularly permuted GTPase that has been reported as Arabidopsis thaliana NITRIC OXIDE ASSOCIATED 1 (AtNOA1) and RESISTANT TO INHIBITION BY FOSMIDOMYCIN 1 (RIF1). Biochemical evidence showed that SVR10/AtNOA1/RIF1 likely localizes to the chloroplast stroma. We further demonstrate that the mutant of a close homologue of SVR10/AtNOA1/RIF1, BRASSINAZOLE INSENSITIVE PALE GREEN 2 (BPG2), can also suppress var2 leaf variegation. Mutants of SVR10 and BPG2 are impaired in photosynthesis and the accumulation of chloroplast proteins. Interestingly, two-dimensional blue native gel analysis showed that mutants of SVR10 and BPG2 display defects in the assembly of thylakoid membrane complexes including reduced levels of major photosynthetic complexes and the abnormal accumulation of a chlorophyll-protein supercomplex containing photosystem I. Taken together, our findings suggest that SVR10 and BPG2 are functionally related with VAR2, likely through their potential roles in regulating chloroplast protein homeostasis, and both SVR10 and BPG2 are required for efficient thylakoid protein complex assembly and photosynthesis.
Robust and Fault-Tolerant Linear Parameter-Varying Control of Wind Turbines

DEFF Research Database (Denmark)

Sloth, Christoffer; Esbensen, Thomas; Stoustrup, Jakob

2011-01-01

High performance and reliability are required for wind turbines to be competitive within the energy market. To capture their nonlinear behavior, wind turbines are often modeled using parameter-varying models. In this paper we design and compare multiple linear parameter-varying (LPV) controllers,...
Aplicación de los instrumentos de reincidencia en violencia HCR-20 y SVR-20 en dos grupos de delincuentes colombianos

Directory of Open Access Journals (Sweden)

Ángela Tapias Saldaña

2011-06-01

Full Text Available Esta investigación, de tipo exploratorio, cuenta con un diseño no experimental y transversal o transeccional; tuvo por objeto determinar si los instrumentos de evaluación psicológica forense HCR-20 y SVR-20 discriminan entre un grupo de reincidentes en delitos de acceso carnal violento y un grupo de sujetos judicializados por delitos menores. Hubo presencia de los indicadores, tanto del HCR-20 como del SVR-20, en los grupos. Se encontraron diferencias significativas en los puntajes de los grupos para el SVR- 20, pero no para el HCR-20. Finalmente, se observaron nuevos factores de riesgo, que podrían incluirse en instrumentos forenses.
SVR-based prediction of carbon emissions from energy consumption in Henan Province

Science.gov (United States)

Gou, Guohua

2018-02-01

This paper analyzes the advantage of support vector regression (SVR) in the prediction of carbon emission and establishes the SVR-based carbon emission prediction model. The model is established using the data of Henan’s carbon emissions and influence factors from the 1991 to 2016 to train and test and then predict the carbon emissions from 2017 to 2021. The results show that: from the perspective of carbon emission from energy consumption, it raised 224.876 million tons of carbon dioxide from 1991 to 2016, and the predicted increment from 2017 to 2021 is 30.5563million tons with an average annual growth rate at 3%. From the perspective of growth rate among the six factors related to carbon emissions it is proved that population urbanization rate per capital GDP and energy consumption per unit of GDP influences the growth rate of carbon emissions less than the proportion of secondary industry and coal consumption ratio of carbon. Finally some suggestions are proposed for the carbon emission reduction of Henan Province.
Constitutive Equation with Varying Parameters for Superplastic Flow Behavior

Science.gov (United States)

Guan, Zhiping; Ren, Mingwen; Jia, Hongjie; Zhao, Po; Ma, Pinkui

2014-03-01

In this study, constitutive equations for superplastic materials with an extra large elongation were investigated through mechanical analysis. From the view of phenomenology, firstly, some traditional empirical constitutive relations were standardized by restricting some strain paths and parameter conditions, and the coefficients in these relations were strictly given new mechanical definitions. Subsequently, a new, general constitutive equation with varying parameters was theoretically deduced based on the general mechanical equation of state. The superplastic tension test data of Zn-5%Al alloy at 340 °C under strain rates, velocities, and loads were employed for building a new constitutive equation and examining its validity. Analysis results indicated that the constitutive equation with varying parameters could characterize superplastic flow behavior in practical superplastic forming with high prediction accuracy and without any restriction of strain path or deformation condition, showing good industrial or scientific interest. On the contrary, those empirical equations have low prediction capabilities due to constant parameters and poor applicability because of the limit of special strain path or parameter conditions based on strict phenomenology.
varying elastic parameters distributions

KAUST Repository

Moussawi, Ali

2014-12-01

The experimental identication of mechanical properties is crucial in mechanics for understanding material behavior and for the development of numerical models. Classical identi cation procedures employ standard shaped specimens, assume that the mechanical elds in the object are homogeneous, and recover global properties. Thus, multiple tests are required for full characterization of a heterogeneous object, leading to a time consuming and costly process. The development of non-contact, full- eld measurement techniques from which complex kinematic elds can be recorded has opened the door to a new way of thinking. From the identi cation point of view, suitable methods can be used to process these complex kinematic elds in order to recover multiple spatially varying parameters through one test or a few tests. The requirement is the development of identi cation techniques that can process these complex experimental data. This thesis introduces a novel identi cation technique called the constitutive compatibility method. The key idea is to de ne stresses as compatible with the observed kinematic eld through the chosen class of constitutive equation, making possible the uncoupling of the identi cation of stress from the identi cation of the material parameters. This uncoupling leads to parametrized solutions in cases where 5 the solution is non-unique (due to unknown traction boundary conditions) as demonstrated on 2D numerical examples. First the theory is outlined and the method is demonstrated in 2D applications. Second, the method is implemented within a domain decomposition framework in order to reduce the cost for processing very large problems. Finally, it is extended to 3D numerical examples. Promising results are shown for 2D and 3D problems.
Tracking time-varying parameters with local regression

DEFF Research Database (Denmark)

Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov

2000-01-01

This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....
Linear Parameter Varying Control of Induction Motors

DEFF Research Database (Denmark)

Trangbæk, Klaus

The subject of this thesis is the development of linear parameter varying (LPV) controllers and observers for control of induction motors. The induction motor is one of the most common machines in industrial applications. Being a highly nonlinear system, it poses challenging control problems...... for high performance applications. This thesis demonstrates how LPV control theory provides a systematic way to achieve good performance for these problems. The main contributions of this thesis are the application of the LPV control theory to induction motor control as well as various contributions...
Predictive validity of the SVR-20 and Static-99 in a Dutch sample of treated sex offenders

NARCIS (Netherlands)

de Vogel, V.; de Ruiter, C.; van Beek, D.; Mead, G.

2004-01-01

In this retrospective study, the interrater reliability and redictive validity of 2 risk assessment instruments for sexual violence are presented. The SVR-20, an instrument for structured professional judgment, and the Static-99, an actuarial risk assessment instrument, were coded from file
Long memory of financial time series and hidden Markov models with time-varying parameters

DEFF Research Database (Denmark)

Nystrup, Peter; Madsen, Henrik; Lindström, Erik

Hidden Markov models are often used to capture stylized facts of daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior for the ability to reproduce the stylized...... facts have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time-varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared...... daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step predictions....
Modelling tourists arrival using time varying parameter

Science.gov (United States)

Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.

2017-06-01

The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.
Control of Linear Parameter Varying Systems with Applications

CERN Document Server

Mohammadpour, Javad

2012-01-01

Control of Linear Parameter Varying Systems with Applications compiles state-of-the-art contributions on novel analytical and computational methods to address system modeling and identification, complexity reduction, performance analysis and control design for time-varying and nonlinear systems in the LPV framework. The book has an interdisciplinary character by emphasizing techniques that can be commonly applied in various engineering fields. It also includes a rich collection of illustrative applications in diverse domains to substantiate the effectiveness of the design methodologies and provide pointers to open research directions. The book is divided into three parts. The first part collects chapters of a more tutorial character on the background of LPV systems modeling and control. The second part gathers chapters devoted to the theoretical advancement of LPV analysis and synthesis methods to cope with the design constraints such as uncertainties and time delay. The third part of the volume showcases con...
Nonlinear systems time-varying parameter estimation: Application to induction motors

Energy Technology Data Exchange (ETDEWEB)

Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)

2008-11-15

In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)

Interferon-free treatment for patients with chronic hepatitis C and autoimmune liver disease: higher SVR rates with special precautions for deterioration of autoimmune hepatitis.

Science.gov (United States)

Kanda, Tatsuo; Yasui, Shin; Nakamura, Masato; Nakamoto, Shingo; Takahashi, Koji; Wu, Shuang; Sasaki, Reina; Haga, Yuki; Ogasawara, Sadahisa; Saito, Tomoko; Kobayashi, Kazufumi; Kiyono, Soichiro; Ooka, Yoshihiko; Suzuki, Eiichiro; Chiba, Tetsuhiro; Maruyama, Hitoshi; Imazeki, Fumio; Moriyama, Mitsuhiko; Kato, Naoya

2018-02-20

Interferon-free treatment can achieve higher sustained virological response (SVR) rates, even in patients in whom hepatitis C virus (HCV) could not be eradicated in the interferon treatment era. Immune restoration in the liver is occasionally associated with HCV infection. We examined the safety and effects of interferon-free regimens on HCV patients with autoimmune liver diseases. All 7 HCV patients with autoimmune hepatitis (AIH) completed treatment and achieved SVR. Three patients took prednisolone (PSL) at baseline, and 3 did not take PSL during interferon-free treatment. In one HCV patient with AIH and cirrhosis, PSL were not administered at baseline, but she needed to take 40 mg/day PSL at week 8 for liver dysfunction. She also complained back pain and was diagnosed with vasospastic angina by coronary angiography at week 11. However, she completed interferon-free treatment. All 5 HCV patients with primary biliary cholangitis (PBC) completed treatment and achieved SVR. Three of these HCV patients with PBC were treated with UDCA during interferon-free treatment. Interferon-free regimens could result in higher SVR rates in HCV patients with autoimmune liver diseases. As interferon-free treatment for HCV may have an effect on hepatic immunity and activity of the autoimmune liver diseases, careful attention should be paid to unexpected adverse events in their treatments. Total 12 patients with HCV and autoimmune liver diseases [7 AIH and PBC], who were treated with interferon-free regimens, were retrospectively analyzed.
Structured Linear Parameter Varying Control of Wind Turbines

DEFF Research Database (Denmark)

Adegas, Fabiano Daher; Sloth, Christoffer; Stoustrup, Jakob

2012-01-01

High performance and reliability are required for wind turbines to be competitive within the energy market. To capture their nonlinear behavior, wind turbines are often modeled using parameter-varying models. In this chapter, a framework for modelling and controller design of wind turbines is pre...... in the controller synthesis are solved by an iterative LMI-based algorithm. The resulting controllers can also be easily implemented in practice due to low data storage and simple math operations. The performance of the LPV controllers is assessed by nonlinear simulations results....
Nonlinear control of linear parameter varying systems with applications to hypersonic vehicles

Science.gov (United States)

Wilcox, Zachary Donald

The focus of this dissertation is to design a controller for linear parameter varying (LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the interplay between control performance and the structural dynamics design. Specifically a Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded, nonvanishing disturbances. The hypersonic vehicle has time varying parameters, specifically temperature profiles, and its dynamics can be reduced to an LPV system with additive disturbances. Since the HSV can be modeled as an LPV system the proposed control design is directly applicable. The control performance is directly examined through simulations. A wide variety of applications exist that can be effectively modeled as LPV systems. In particular, flight systems have historically been modeled as LPV systems and associated control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs), linear fractional transformations (LFT), and mu-types. However, as the type of flight environments and trajectories become more demanding, the traditional LPV controllers may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in the dynamics. HSV flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics, the aerothermoelastic effects are modeled by a temperature dependent, parameter varying state-space representation with added disturbances. The model includes an uncertain parameter varying state matrix, an uncertain parameter varying non-square (column deficient) input matrix, and an additive bounded disturbance. In this dissertation, a robust dynamic controller is formulated for a uncertain and disturbed LPV system. The developed
Time-varying parameter models for catchments with land use change: the importance of model structure

Science.gov (United States)

Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid

2018-05-01

Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
Time-varying parameter models for catchments with land use change: the importance of model structure

Directory of Open Access Journals (Sweden)

S. Pathiraja

2018-05-01

Full Text Available Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2 in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
A dynamic particle filter-support vector regression method for reliability prediction

International Nuclear Information System (INIS)

Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

2013-01-01

Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR
Risk adjusted receding horizon control of constrained linear parameter varying systems

NARCIS (Netherlands)

Sznaier, M.; Lagoa, C.; Stoorvogel, Antonie Arij; Li, X.

2005-01-01

In the past few years, control of Linear Parameter Varying Systems (LPV) has been the object of considerable attention, as a way of formalizing the intuitively appealing idea of gain scheduling control for nonlinear systems. However, currently available LPV techniques are both computationally
Identification of Affine Linear Parameter Varying Models for Adaptive Interventions in Fibromyalgia Treatment.

Science.gov (United States)

Dos Santos, P Lopes; Deshpande, Sunil; Rivera, Daniel E; Azevedo-Perdicoúlis, T-P; Ramos, J A; Younger, Jarred

2013-12-31

There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.
Multiobjective Optimization for Fixture Locating Layout of Sheet Metal Part Using SVR and NSGA-II

Directory of Open Access Journals (Sweden)

Yuan Yang

2017-01-01

Full Text Available Fixture plays a significant role in determining the sheet metal part (SMP spatial position and restraining its excessive deformation in many manufacturing operations. However, it is still a difficult task to design and optimize SMP fixture locating layout at present because there exist multiple conflicting objectives and excessive computational cost of finite element analysis (FEA during the optimization process. To this end, a new multiobjective optimization method for SMP fixture locating layout is proposed in this paper based on the support vector regression (SVR surrogate model and the elitist nondominated sorting genetic algorithm (NSGA-II. By using ABAQUS™ Python script interface, a parametric FEA model is established. And the fixture locating layout is treated as design variables, while the overall deformation and maximum deformation of SMP under external forces are as the multiple objective functions. First, a limited number of training and testing samples are generated by combining Latin hypercube design (LHD with FEA. Second, two SVR prediction models corresponding to the multiple objectives are established by learning from the limited training samples and are integrated as the multiobjective optimization surrogate model. Third, NSGA-II is applied to determine the Pareto optimal solutions of SMP fixture locating layout. Finally, a multiobjective optimization for fixture locating layout of an aircraft fuselage skin case is conducted to illustrate and verify the proposed method.
Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

Directory of Open Access Journals (Sweden)

Huiguo Chen

2017-01-01

Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.
Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

DEFF Research Database (Denmark)

Nystrup, Peter; Madsen, Henrik; Lindström, Erik

2016-01-01

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive...... to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations....
Adaptive control of chaotic systems with stochastic time varying unknown parameters

Energy Technology Data Exchange (ETDEWEB)

Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

2008-10-15

In this paper based on the Lyapunov stability theorem, an adaptive control scheme is proposed for stabilizing the unstable periodic orbits (UPO) of chaotic systems. It is assumed that the chaotic system has some linearly dependent unknown parameters which are stochastically time varying. The stochastic parameters are modeled through the Weiner process derivative. To demonstrate the effectiveness of the proposed technique it has been applied to the Lorenz, Chen and Rossler dynamical systems, as some case studies. Simulation results indicate that the proposed adaptive controller has a high performance in stabilizing the UPO of chaotic systems in noisy environment.
Stability of neutrino parameters and self-complementarity relation with varying SUSY breaking scale

Science.gov (United States)

Singh, K. Sashikanta; Roy, Subhankar; Singh, N. Nimai

2018-03-01

The scale at which supersymmetry (SUSY) breaks (ms) is still unknown. The present article, following a top-down approach, endeavors to study the effect of varying ms on the radiative stability of the observational parameters associated with the neutrino mixing. These parameters get additional contributions in the minimal supersymmetric model (MSSM). A variation in ms will influence the bounds for which the Standard Model (SM) and MSSM work and hence, will account for the different radiative contributions received from both sectors, respectively, while running the renormalization group equations (RGE). The present work establishes the invariance of the self complementarity relation among the three mixing angles, θ13+θ12≈θ23 against the radiative evolution. A similar result concerning the mass ratio, m2:m1 is also found to be valid. In addition to varying ms, the work incorporates a range of different seesaw (SS) scales and tries to see how the latter affects the parameters.
Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

Science.gov (United States)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing

2014-12-01

Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.
Linear parameter-varying control for engineering applications

CERN Document Server

White, Andrew P; Choi, Jongeun

2013-01-01

The objective of this brief is to carefully illustrate a procedure of applying linear parameter-varying (LPV) control to a class of dynamic systems via a systematic synthesis of gain-scheduling controllers with guaranteed stability and performance. The existing LPV control theories rely on the use of either H-infinity or H2 norm to specify the performance of the LPV system. The challenge that arises with LPV control for engineers is twofold. First, there is no systematic procedure for applying existing LPV control system theory to solve practical engineering problems from modeling to control design. Second, there exists no LPV control synthesis theory to design LPV controllers with hard constraints. For example, physical systems usually have hard constraints on their required performance outputs along with their sensors and actuators. Furthermore, the H-infinity and H2 performance criteria cannot provide hard constraints on system outputs. As a result, engineers in industry could find it difficult to utiliz...
Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach

Directory of Open Access Journals (Sweden)

Zhi-Sai Ma

2017-01-01

Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.
Linear parameter varying representations for nonlinear control design

Science.gov (United States)

Carter, Lance Huntington

Linear parameter varying (LPV) systems are investigated as a framework for gain-scheduled control design and optimal hybrid control. An LPV system is defined as a linear system whose dynamics depend upon an a priori unknown but measurable exogenous parameter. A gain-scheduled autopilot design is presented for a bank-to-turn (BTT) missile. The method is novel in that the gain-scheduled design does not involve linearizations about operating points. Instead, the missile dynamics are brought to LPV form via a state transformation. This idea is applied to the design of a coupled longitudinal/lateral BTT missile autopilot. The pitch and yaw/roll dynamics are separately transformed to LPV form, where the cross axis states are treated as "exogenous" parameters. These are actually endogenous variables, so such a plant is called "quasi-LPV." Once in quasi-LPV form, a family of robust controllers using mu synthesis is designed for both the pitch and yaw/roll channels, using angle-of-attack and roll rate as the scheduling variables. The closed-loop time response is simulated using the original nonlinear model and also using perturbed aerodynamic coefficients. Modeling and control of engine idle speed is investigated using LPV methods. It is shown how generalized discrete nonlinear systems may be transformed into quasi-LPV form. A discrete nonlinear engine model is developed and expressed in quasi-LPV form with engine speed as the scheduling variable. An example control design is presented using linear quadratic methods. Simulations are shown comparing the LPV based controller performance to that using PID control. LPV representations are also shown to provide a setting for hybrid systems. A hybrid system is characterized by control inputs consisting of both analog signals and discrete actions. A solution is derived for the optimal control of hybrid systems with generalized cost functions. This is shown to be computationally intensive, so a suboptimal strategy is proposed that
Machine Learning of the Reactor Core Loading Pattern Critical Parameters

Directory of Open Access Journals (Sweden)

Krešimir Trontl

2008-01-01

Full Text Available The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR, which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.
Machine Learning of the Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy
Scalable Video Streaming Adaptive to Time-Varying IEEE 802.11 MAC Parameters

Science.gov (United States)

Lee, Kyung-Jun; Suh, Doug-Young; Park, Gwang-Hoon; Huh, Jae-Doo

This letter proposes a QoS control method for video streaming service over wireless networks. Based on statistical analysis, the time-varying MAC parameters highly related to channel condition are selected to predict available bitrate. Adaptive bitrate control of scalably-encoded video guarantees continuity in streaming service even if the channel condition changes abruptly.

Machine learning of the reactor core loading pattern critical parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2007-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code. (author)
LMI-based gain scheduled controller synthesis for a class of linear parameter varying systems

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon; Anderson, Brian; Lanzon, Alexander

2006-01-01

This paper presents a novel method for constructing controllers for a class of single-input multiple-output (SIMO) linear parameter varying (LPV) systems. This class of systems encompasses many physical systems, in particular systems where individual components vary with time, and is therefore...... of significant practical relevance to control designers. The control design presented in this paper has the properties that the system matrix of the closed loop is multi-affine in the various scalar parameters, and that the resulting controller ensures a certain degree of stability for the closed loop even when...... as a standard linear time-invariant (LTI) design combined with a set of linear matrix inequalities, which can be solved efficiently with software tools. The design procedure is illustrated by a numerical example....
An improved robust model predictive control for linear parameter-varying input-output models

NARCIS (Netherlands)

Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

2018-01-01

This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal
Forecasting systems reliability based on support vector regression with genetic algorithms

International Nuclear Information System (INIS)

Chen, K.-Y.

2007-01-01

This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error
Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

Science.gov (United States)

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.
Genetic algorithm–based varying parameter linear quadratic regulator control for four-wheel independent steering vehicle

Directory of Open Access Journals (Sweden)

Linlin Gao

2015-11-01

Full Text Available From the perspective of vehicle dynamics, the four-wheel independent steering vehicle dynamics stability control method is studied, and a four-wheel independent steering varying parameter linear quadratic regulator control system is proposed with the help of expert control method. In the article, a four-wheel independent steering linear quadratic regulator controller for model following purpose is designed first. Then, by analyzing the four-wheel independent steering vehicle dynamic characteristics and the influence of linear quadratic regulator control parameters on control performance, a linear quadratic regulator control parameter adjustment strategy based on vehicle steering state is proposed to achieve the adaptive adjustment of linear quadratic regulator control parameters. In addition, to further improve the control performance, the proposed varying parameter linear quadratic regulator control system is optimized by genetic algorithm. Finally, simulation studies have been conducted by applying the proposed control system to the 8-degree-of-freedom four-wheel independent steering vehicle dynamics model. The simulation results indicate that the proposed control system has better performance and robustness and can effectively improve the stability and steering safety of the four-wheel independent steering vehicle.
A Hilbert transform method for parameter identification of time-varying structures with observer techniques

International Nuclear Information System (INIS)

Wang, Zuo-Cai; Ren, Wei-Xin; Chen, Gen-Da

2012-01-01

This paper presents a recursive Hilbert transform method for the time-varying property identification of large-scale shear-type buildings with limited sensor deployments. An observer technique is introduced to estimate the building responses from limited available measurements. For an n-story shear-type building with l measurements (l ≤ n), the responses of other stories without measurements can be estimated based on the first r mode shapes (r ≤ l) as-built conditions and l measurements. Both the measured responses and evaluated responses and their Hilbert transforms are then used to track any variation of structural parameters of a multi-story building over time. Given floor masses, both the stiffness and damping coefficients of the building are identified one-by-one from the top to the bottom story. When variations of parameters are detected, a new developed branch-and-bound technique can be used to update the first r mode shapes with the identified parameters. A 60-story shear building with abruptly varying stiffness at different floors is simulated as an example. The numerical results indicate that the proposed method can detect variations of the parameters of large-scale shear-type buildings with limited sensor deployments at appropriate locations. (paper)
Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy

DEFF Research Database (Denmark)

Callot, Laurent; Kristensen, Johannes Tang

the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule.We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response...
Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN and Supporting Vector Machine (SVR

Directory of Open Access Journals (Sweden)

Seyedehelham Sadatiseyedmahalleh

2016-02-01

Full Text Available This research examines and proves this effectiveness connected with artificial neural networks (ANNs as an alternative approach to the use of Support Vector Machine (SVR in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.
Linear Parameter Varying Versus Linear Time Invariant Reduced Order Controller Design of Turboprop Aircraft Dynamics

Directory of Open Access Journals (Sweden)

Widowati

2012-07-01

Full Text Available The applicability of parameter varying reduced order controllers to aircraft model is proposed. The generalization of the balanced singular perturbation method of linear time invariant (LTI system is used to reduce the order of linear parameter varying (LPV system. Based on the reduced order model the low-order LPV controller is designed by using synthesis technique. The performance of the reduced order controller is examined by applying it to lateral-directional control of aircraft model having 20th order. Furthermore, the time responses of the closed loop system with reduced order LPV controllers and reduced order LTI controller is compared. From the simulation results, the 8th order LPV controller can maintain stability and to provide the same level of closed-loop systems performance as the full-order LPV controller. It is different with the reduced-order LTI controller that cannot maintain stability and performance for all allowable parameter trajectories.
Parameter Estimation of a Closed Loop Coupled Tank Time Varying System using Recursive Methods

International Nuclear Information System (INIS)

Basir, Siti Nora; Yussof, Hanafiah; Shamsuddin, Syamimi; Selamat, Hazlina; Zahari, Nur Ismarrubie

2013-01-01

This project investigates the direct identification of closed loop plant using discrete-time approach. The uses of Recursive Least Squares (RLS), Recursive Instrumental Variable (RIV) and Recursive Instrumental Variable with Centre-Of-Triangle (RIV + COT) in the parameter estimation of closed loop time varying system have been considered. The algorithms were applied in a coupled tank system that employs covariance resetting technique where the time of parameter changes occur is unknown. The performances of all the parameter estimation methods, RLS, RIV and RIV + COT were compared. The estimation of the system whose output was corrupted with white and coloured noises were investigated. Covariance resetting technique successfully executed when the parameters change. RIV + COT gives better estimates than RLS and RIV in terms of convergence and maximum overshoot
A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

International Nuclear Information System (INIS)

Wang Jianzhou; Zhu Wenjin; Zhang Wenyu; Sun Donghuai

2009-01-01

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined ε-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the ε-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.
A trend fixed on firstly and seasonal adjustment model combined with the epsilon-SVR for short-term forecasting of electricity demand

Energy Technology Data Exchange (ETDEWEB)

Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhu Wenjin, E-mail: crying.1@hotmail.co [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

2009-11-15

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined epsilon-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the epsilon-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.
A trend fixed on firstly and seasonal adjustment model combined with the {epsilon}-SVR for short-term forecasting of electricity demand

Energy Technology Data Exchange (ETDEWEB)

Wang, Jianzhou; Zhu, Wenjin [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang, Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun, Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

2009-11-15

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined {epsilon}-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the {epsilon}-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved. (author)
In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models

NARCIS (Netherlands)

Blasques, F.; Koopman, S.J.; Lasak, K.A.; Lucas, A.

2016-01-01

We study the performances of alternative methods for calculating in-sample confidence and out-of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty, while the out-of-sample bands reflect not only parameter uncertainty, but also innovation
Nonlinear Parameter-Varying AeroServoElastic Reduced Order Model for Aerostructural Sensing and Control, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate nonlinear, parameter-varying (PV),...
A method for separating seismo-ionospheric TEC outliers from heliogeomagnetic disturbances by using nu-SVR

Energy Technology Data Exchange (ETDEWEB)

Pattisahusiwa, Asis [Bandung Institute of Technology (Indonesia); Liong, The Houw; Purqon, Acep [Earth physics and complex systems research group, Bandung Institute of Technology (Indonesia)

2015-09-30

Seismo-Ionospheric is a study of ionosphere disturbances associated with seismic activities. In many previous researches, heliogeomagnetic or strong earthquake activities can caused the disturbances in the ionosphere. However, it is difficult to separate these disturbances based on related sources. In this research, we proposed a method to separate these disturbances/outliers by using nu-SVR with the world-wide GPS data. TEC data related to the 26th December 2004 Sumatra and the 11th March 2011 Honshu earthquakes had been analyzed. After analyzed TEC data in several location around the earthquake epicenter and compared with geomagnetic data, the method shows a good result in the average to detect the source of these outliers. This method is promising to use in the future research.
A 3D domain decomposition approach for the identification of spatially varying elastic material parameters

KAUST Repository

Moussawi, Ali; Lubineau, Gilles; Xu, Jiangping; Pan, Bing

2015-01-01

Summary: The post-treatment of (3D) displacement fields for the identification of spatially varying elastic material parameters is a large inverse problem that remains out of reach for massive 3D structures. We explore here the potential
Nonlinear Parameter-Varying AeroServoElastic Reduced Order Model for Aerostructural Sensing and Control, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate parameter-varying (PV), aeroservoelastic (ASE)...
A receding horizon scheme for discrete-time polytopic linear parameter varying systems in networked architectures

International Nuclear Information System (INIS)

Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

2014-01-01

This paper proposes a Model Predictive Control (MPC) strategy to address regulation problems for constrained polytopic Linear Parameter Varying (LPV) systems subject to input and state constraints in which both plant measurements and command signals in the loop are sent through communication channels subject to time-varying delays (Networked Control System (NCS)). The results here proposed represent a significant extension to the LPV framework of a recent Receding Horizon Control (RHC) scheme developed for the so-called robust case. By exploiting the parameter availability, the pre-computed sequences of one- step controllable sets inner approximations are less conservative than the robust counterpart. The resulting framework guarantees asymptotic stability and constraints fulfilment regardless of plant uncertainties and time-delay occurrences. Finally, experimental results on a laboratory two-tank test-bed show the effectiveness of the proposed approach

Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations

KAUST Repository

Cao, Jiguo; Huang, Jianhua Z.; Wu, Hulin

2012-01-01

Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.
Pattern formation in individual-based systems with time-varying parameters

Science.gov (United States)

Ashcroft, Peter; Galla, Tobias

2013-12-01

We study the patterns generated in finite-time sweeps across symmetry-breaking bifurcations in individual-based models. Similar to the well-known Kibble-Zurek scenario of defect formation, large-scale patterns are generated when model parameters are varied slowly, whereas fast sweeps produce a large number of small domains. The symmetry breaking is triggered by intrinsic noise, originating from the discrete dynamics at the microlevel. Based on a linear-noise approximation, we calculate the characteristic length scale of these patterns. We demonstrate the applicability of this approach in a simple model of opinion dynamics, a model in evolutionary game theory with a time-dependent fitness structure, and a model of cell differentiation. Our theoretical estimates are confirmed in simulations. In further numerical work, we observe a similar phenomenon when the symmetry-breaking bifurcation is triggered by population growth.
On-Line Flutter Prediction Tool for Wind Tunnel Flutter Testing using Parameter Varying Estimation Methodology, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool for wind tunnel model using the parameter varying estimation (PVE) technique to...
Calculation of the separate parameters of a countercurrent centrifuge with an axially varying internal flow

International Nuclear Information System (INIS)

Migliavacca, S.C.P.

1991-01-01

A review of the isotope separation theory for the countercurrent gas centrifuge is presented. The diffusion-convection equation is solved according to the ONSAGER-COHEN solution for the constant internal flow and adapted to an axially varying countercurrent flow. Based on that theory, a numerical program is developed for the calculation of the isotopic compositions and the separative parameters of the centrifuge. The influence of the feed flow and the internal parameters. Like cut and countercurrent flow, on the separative parameters is then analysed for a model-centrifuge, which afterwards is optimized with respect to its separative power. Finally, a comparison between the present calculation procedure and some published results, provided by different theories, shows deviations lower then 20%. (author)
Effect of varying geometrical parameters of trapezoidal corrugated-core sandwich structure

Directory of Open Access Journals (Sweden)

Zaid N.Z.M.

2017-01-01

Full Text Available Sandwich structure is an attractive alternative that increasingly used in the transportation and aerospace industry. Corrugated-core with trapezoidal shape allows enhancing the damage resistance to the sandwich structure, but on the other hand, it changes the structural response of the sandwich structure. The aim of this paper is to study the effect of varying geometrical parameters of trapezoidal corrugated-core sandwich structure under compression loading. The corrugated-core specimen was fabricated using press technique, following the shape of trapezoidal shape. Two different materials were used in the study, glass fibre reinforced plastic (GFRP and carbon fibre reinforced plastic (CFRP. The result shows that the mechanical properties of the core in compression loading are sensitive to the variation of a number of unit cells and the core thickness.
Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler

International Nuclear Information System (INIS)

Wei, Zhongbao; Li, Xiaolu; Xu, Lijun; Cheng, Yanting

2013-01-01

This paper focuses on NO x emission prediction and operating parameters optimization for coal-fired boilers. Support Vector Regression (SVR) model based on CGA (Conventional Genetic Algorithm) was proposed to model the relationship between the operating parameters and the concentration of NO x emission. Then CGA and two modified algorithms, the Quantum Genetic Algorithm (QGA) and SAGA (Simulated Annealing Genetic Algorithm), were employed to optimize the operating parameters of the coal-fired boiler to reduce NO x emission. The results showed that the proposed SVR model was more accurate than the widely used Artificial Neural Network (ANN) model when employed to predict the concentration of NO x emission. The mean relative error and correlation coefficient calculated by the proposed SVR model were 2.08% and 0.95, respectively. Among the three optimization algorithms implemented in this paper, the SAGA showed superiority to the other two algorithms considering the quality of solution within a given computing time. The SVR plus SAGA method was preferable to predict the concentration of NO x emission and further to optimize the operating parameters to achieve low NO x emission for coal-fired boilers. - Highlights: • The CGA based SVR model is proposed to predict the concentration of NO x emission. • The CGA based SVR model performs better than the widely used ANN model. • CGA and two modified algorithms are compared to optimize the parameters. • The SAGA is preferable for its high quality of solution and low computing time. • The SVR plus SAGA is successfully employed to optimize the operating parameters
Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

Science.gov (United States)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.
ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL.

Science.gov (United States)

Liang, Hua; Miao, Hongyu; Wu, Hulin

2010-03-01

Modeling viral dynamics in HIV/AIDS studies has resulted in deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper, we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies. We applied the proposed techniques to estimate the key HIV viral dynamic parameters for two individual AIDS patients treated with antiretroviral therapies. We demonstrate that HIV viral dynamics can be well characterized and
Analyzing the term structure of interest rates using the dynamic Nelson-Siegel model with time-varying parameters

NARCIS (Netherlands)

Koopman, S.J.; Mallee, M.I.P.; van der Wel, M.

2010-01-01

In this article we introduce time-varying parameters in the dynamic Nelson-Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson-Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive
Bianchi Type-V Bulk Viscous Cosmic String in f(R,T Gravity with Time Varying Deceleration Parameter

Directory of Open Access Journals (Sweden)

Bïnaya K. Bishi

2015-01-01

Full Text Available We study the Bianchi type-V string cosmological model with bulk viscosity in f(R,T theory of gravity by considering a special form and linearly varying deceleration parameter. This is an extension of the earlier work of Naidu et al., 2013, where they have constructed the model by considering a constant deceleration parameter. Here we find that the cosmic strings do not survive in both models. In addition we study some physical and kinematical properties of both models. We observe that in one of our models these properties are identical to the model obtained by Naidu et al., 2013, and in the other model the behavior of these parameters is different.
Dynamical bifurcation in a system of coupled oscillators with slowly varying parameters

Directory of Open Access Journals (Sweden)

Igor Parasyuk

2016-08-01

Full Text Available This paper deals with a fast-slow system representing n nonlinearly coupled oscillators with slowly varying parameters. We find conditions which guarantee that all omega-limit sets near the slow surface of the system are equilibria and invariant tori of all dimensions not exceeding n, the tori of dimensions less then n being hyperbolic. We show that a typical trajectory demonstrates the following transient process: while its slow component is far from the stationary points of the slow vector field, the fast component exhibits damping oscillations; afterwards, the former component enters and stays in a small neighborhood of some stationary point, and the oscillation amplitude of the latter begins to increase; eventually the trajectory is attracted by an n-dimesional invariant torus and a multi-frequency oscillatory regime is established.
Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Meiping Wang

2016-01-01

Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.
Device and performance parameters of Cu(In,Ga)(Se,S)2-based solar cells with varying i-ZnO layer thickness

International Nuclear Information System (INIS)

Macabebe, E.Q.B.; Sheppard, C.J.; Dyk, E.E. van

2009-01-01

In pursuit of low-cost and highly efficient thin film solar cells, Cu(In,Ga)(Se,S) 2 /CdS/i-ZnO/ZnO:Al (CIGSS) solar cells were fabricated using a two-step process. The thickness of i-ZnO layer was varied from 0 to 454 nm. The current density-voltage (J-V) characteristics of the devices were measured, and the device and performance parameters of the solar cells were obtained from the J-V curves to analyze the effect of varying i-ZnO layer thickness. The device parameters were determined using a parameter extraction method that utilized particle swarm optimization. The method is a curve-fitting routine that employed the two-diode model. The J-V curves of the solar cells were fitted with the model and the parameters were determined. Results show that as the thickness of i-ZnO was increased, the average efficiency and the fill factor (FF) of the solar cells increase. Device parameters reveal that although the series resistance increased with thicker i-ZnO layer, the solar cells absorbed more photons resulting in higher short-circuit current density (J sc ) and, consequently, higher photo-generated current density (J L ). For solar cells with 303-454 nm-thick i-ZnO layer, the best devices achieved efficiency between 15.24% and 15.73% and the fill factor varied between 0.65 and 0.67.
Robust control and linear parameter varying approaches application to vehicle dynamics

CERN Document Server

Gaspar, Peter; Bokor, József

2013-01-01

Vehicles are complex systems (non-linear, multi-variable) where the abundance of embedded controllers should ensure better security. This book aims at emphasizing the interest and potential of Linear Parameter Varying methods within the framework of vehicle dynamics, e.g. · proposed control-oriented model, complex enough to handle some system non linearities but still simple for control or observer design, · take into account the adaptability of the vehicle's response to driving situations, to the driver request and/or to the road sollicitations, · manage interactions between various actuators to optimize the dynamic behavior of vehicles. This book results from the 32th International Summer School in Automatic that held in Grenoble, France, in September 2011, where recent methods (based on robust control and LPV technics), then applied to the control of vehicle dynamics, have been presented. After some theoretical background and a view on so...
Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Directory of Open Access Journals (Sweden)

Seunghyeon Wang

2018-02-01

Full Text Available In many countries, DR (Demand Response has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing. Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP’s purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm–SVR (Support Vector Regression. The model is applied to a hospital in Korea and 34,636 data sets (1 year are collected while 31,756 (11 months sets are used for training and 2880 sets (1 month are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE, which satisfies the Korea Energy Agency’s and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers error allowance range of ±30%, and ±20% respectively.
Device and performance parameters of Cu(In,Ga)(Se,S){sub 2}-based solar cells with varying i-ZnO layer thickness

Energy Technology Data Exchange (ETDEWEB)

Macabebe, E.Q.B. [Department of Physics, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031 (South Africa); Sheppard, C.J. [Department of Physics, University of Johannesburg, P.O. Box 524, Auckland Park 2006 (South Africa); Dyk, E.E. van, E-mail: ernest.vandyk@nmmu.ac.z [Department of Physics, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031 (South Africa)

2009-12-01

In pursuit of low-cost and highly efficient thin film solar cells, Cu(In,Ga)(Se,S){sub 2}/CdS/i-ZnO/ZnO:Al (CIGSS) solar cells were fabricated using a two-step process. The thickness of i-ZnO layer was varied from 0 to 454 nm. The current density-voltage (J-V) characteristics of the devices were measured, and the device and performance parameters of the solar cells were obtained from the J-V curves to analyze the effect of varying i-ZnO layer thickness. The device parameters were determined using a parameter extraction method that utilized particle swarm optimization. The method is a curve-fitting routine that employed the two-diode model. The J-V curves of the solar cells were fitted with the model and the parameters were determined. Results show that as the thickness of i-ZnO was increased, the average efficiency and the fill factor (FF) of the solar cells increase. Device parameters reveal that although the series resistance increased with thicker i-ZnO layer, the solar cells absorbed more photons resulting in higher short-circuit current density (J{sub sc}) and, consequently, higher photo-generated current density (J{sub L}). For solar cells with 303-454 nm-thick i-ZnO layer, the best devices achieved efficiency between 15.24% and 15.73% and the fill factor varied between 0.65 and 0.67.
A 3D domain decomposition approach for the identification of spatially varying elastic material parameters

KAUST Repository

Moussawi, Ali

2015-02-24

Summary: The post-treatment of (3D) displacement fields for the identification of spatially varying elastic material parameters is a large inverse problem that remains out of reach for massive 3D structures. We explore here the potential of the constitutive compatibility method for tackling such an inverse problem, provided an appropriate domain decomposition technique is introduced. In the method described here, the statically admissible stress field that can be related through the known constitutive symmetry to the kinematic observations is sought through minimization of an objective function, which measures the violation of constitutive compatibility. After this stress reconstruction, the local material parameters are identified with the given kinematic observations using the constitutive equation. Here, we first adapt this method to solve 3D identification problems and then implement it within a domain decomposition framework which allows for reduced computational load when handling larger problems.
Performance Testing of Suspension Plasma Sprayed Thermal Barrier Coatings Produced with Varied Suspension Parameters

Directory of Open Access Journals (Sweden)

Nicholas Curry

2015-07-01

Full Text Available Suspension plasma spraying has become an emerging technology for the production of thermal barrier coatings for the gas turbine industry. Presently, though commercial systems for coating production are available, coatings remain in the development stage. Suitable suspension parameters for coating production remain an outstanding question and the influence of suspension properties on the final coatings is not well known. For this study, a number of suspensions were produced with varied solid loadings, powder size distributions and solvents. Suspensions were sprayed onto superalloy substrates coated with high velocity air fuel (HVAF -sprayed bond coats. Plasma spray parameters were selected to generate columnar structures based on previous experiments and were maintained at constant to discover the influence of the suspension behavior on coating microstructures. Testing of the produced thermal barrier coating (TBC systems has included thermal cyclic fatigue testing and thermal conductivity analysis. Pore size distribution has been characterized by mercury infiltration porosimetry. Results show a strong influence of suspension viscosity and surface tension on the microstructure of the produced coatings.
New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and Time-Varying Delays

Science.gov (United States)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Thangaraj, P.

2013-02-01

This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.
Application of chaotic ant swarm optimization in electric load forecasting

International Nuclear Information System (INIS)

Hong, W.-C.

2010-01-01

Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Directory of Open Access Journals (Sweden)

Zhongyi Hu

2013-01-01

Full Text Available Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA based memetic algorithm (FA-MA to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.
Neutron Buildup Factors Calculation for Support Vector Regression Application in Shielding Analysis

International Nuclear Information System (INIS)

Duckic, P.; Matijevic, M.; Grgic, D.

2016-01-01

In this paper initial set of data for neutron buildup factors determination using Support Vector Regression (SVR) method is prepared. The performance of SVR technique strongly depends on the quality of information used for model training. Thus it is very important to provide representable data to the SVR. SVR is a supervised type of learning so it demands data in the input/output form. In the case of neutron buildup factors estimation, the input parameters are the incident neutron energy, shielding thickness and shielding material and the output parameter is the neutron buildup factor value. So far the initial sets of data for different shielding configurations have been obtained using SCALE4.4 sequence SAS3. However, this results were obtained using group constants, thus the incident neutron energy was determined as the average value for each energy group. Obtained this way, the data provided to the SVR are fewer and therefore insufficient. More valuable information is obtained using SCALE6.2beta5 sequence MAVRIC which can perform calculations for the explicit incident neutron energy, which leads to greater maneuvering possibilities when active learning measures are employed, and consequently improves the quality of the developed SVR model.(author).
Time-varying BRDFs.

Science.gov (United States)

Sun, Bo; Sunkavalli, Kalyan; Ramamoorthi, Ravi; Belhumeur, Peter N; Nayar, Shree K

2007-01-01

The properties of virtually all real-world materials change with time, causing their bidirectional reflectance distribution functions (BRDFs) to be time varying. However, none of the existing BRDF models and databases take time variation into consideration; they represent the appearance of a material at a single time instance. In this paper, we address the acquisition, analysis, modeling, and rendering of a wide range of time-varying BRDFs (TVBRDFs). We have developed an acquisition system that is capable of sampling a material's BRDF at multiple time instances, with each time sample acquired within 36 sec. We have used this acquisition system to measure the BRDFs of a wide range of time-varying phenomena, which include the drying of various types of paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabrics), the accumulation of dusts (household and joint compound) on surfaces, and the melting of materials (chocolate). Analytic BRDF functions are fit to these measurements and the model parameters' variations with time are analyzed. Each category exhibits interesting and sometimes nonintuitive parameter trends. These parameter trends are then used to develop analytic TVBRDF models. The analytic TVBRDF models enable us to apply effects such as paint drying and dust accumulation to arbitrary surfaces and novel materials.
On-Line Identification of Simulation Examples for Forgetting Methods to Track Time Varying Parameters Using the Alternative Covariance Matrix in Matlab

Science.gov (United States)

Vachálek, Ján

2011-12-01

The paper compares the abilities of forgetting methods to track time varying parameters of two different simulated models with different types of excitation. The observed parameters in the simulations are the integral sum of the Euclidean norm, deviation of the parameter estimates from their true values and a selected band prediction error count. As supplementary information, we observe the eigenvalues of the covariance matrix. In the paper we used a modified method of Regularized Exponential Forgetting with Alternative Covariance Matrix (REFACM) along with Directional Forgetting (DF) and three standard regularized methods.
Modeling of Isomerization of C8 Aromatics by Online Least Squares Support Vector Machine%在线最小二乘支持向量机及其在C8芳烃异构化建模中的应用

Institute of Scientific and Technical Information of China (English)

李丽娟; 苏宏业; 褚建

2009-01-01

Trie least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of Cj aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.
Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jianzhou Wang

2015-01-01

Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
Robust control design for active driver assistance systems a linear-parameter-varying approach

CERN Document Server

Gáspár, Péter; Bokor, József; Nemeth, Balazs

2017-01-01

This monograph focuses on control methods that influence vehicle dynamics to assist the driver in enhancing passenger comfort, road holding, efficiency and safety of transport, etc., while maintaining the driver’s ability to override that assistance. On individual-vehicle-component level the control problem is formulated and solved by a unified modelling and design method provided by the linear parameter varying (LPV) framework. The global behaviour desired is achieved by a judicious interplay between the individual components, guaranteed by an integrated control mechanism. The integrated control problem is also formalized and solved in the LPV framework. Most important among the ideas expounded in the book are: application of the LPV paradigm in the modelling and control design methodology; application of the robust LPV design as a unified framework for setting control tasks related to active driver assistance; formulation and solution proposals for the integrated vehicle control problem; proposal for a re...
Effects of varying densities on serum reproductive parameters in pen-reared juvenile female rainbow trout Oncorhynchus mykiss farms

Science.gov (United States)

Hou, Zhishuai; Wen, Haishen; Li, Jifang; He, Feng; Liu, Qun; Wang, Jinhuan; Guan, Biao; Wang, Qinglong

2017-01-01

The primary goal of this study was to assess the effect of varying densities on serum reproductive parameters of immature rainbow trout Oncorhynchus mykiss. Experimental trout were maintained in intensive, pen-reared farms for 300 days in fresh water reservoirs. Initial densities were 4.6, 6.6, and 8.6 kg/m3 (40, 60, 80 ind./m3), indicated as SD1, SD2, SD3, and final densities were 31.1, 40.6, 49.3 kg/m3, respectively. A summary of the ovarian stages were observed by histological examination. Serum E2 (estradiol), T (testosterone) were evaluated by radioimmunoassay and FSH (follicle-stimulating-hormone), LH (luteinizing-hormone), vitellogenin, 17α,20β-P (17α,20βdihydroxy4-pregnen-3-one) were measured by enzyme-linked immunosorbent assay. Our findings demonstrated that ovarian development were retarded (from stage III to stage IV) at highest rearing density (SD3) after 180 days of intensive culture (over 40.6 kg/m3). In addition, we observed an inverse relationship between serum reproductive parameters and rearing density. Furthermore, compared to serum reproductive parameters of SD1, E2, T, FSH, vitellogenin, 17α,20β-P, GSI and LH of two higher density groups decreased firstly and significantly at 60 (over 15.9 kg/m 3 ), 180 (over 31.7 kg/m 3 ), 180 (over 40.6 kg/m3), 240 (over 36 kg/m3), 240 (over 36 kg/m3), 240 (over 45 kg/m3) and 300 (over 49.3 kg/m3) days, respectively. Comparing serum reproductive parameters within the same ovarian development stage of rainbow trout from varying densities revealed that higher population density also led to significantly lower overall serum reproductive parameters. Overall, this study presents the reproductive, endocrinological parameters of juvenile female rainbow trout at high rearing densities and indicates the need for rainbow trout (114.44±5.21 g, 19.69±0.31 cm) that are initially stocked at 6.6 or 8.6 kg/m3 should be classified and subdivided into lower density after 180 days of farming (not over 31.7 kg/m3).
Using support vector regression to predict PM10 and PM2.5

International Nuclear Information System (INIS)

Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

2014-01-01

Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying
Estimating varying coefficients for partial differential equation models.

Science.gov (United States)

Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

2017-09-01

Partial differential equations (PDEs) are used to model complex dynamical systems in multiple dimensions, and their parameters often have important scientific interpretations. In some applications, PDE parameters are not constant but can change depending on the values of covariates, a feature that we call varying coefficients. We propose a parameter cascading method to estimate varying coefficients in PDE models from noisy data. Our estimates of the varying coefficients are shown to be consistent and asymptotically normally distributed. The performance of our method is evaluated by a simulation study and by an empirical study estimating three varying coefficients in a PDE model arising from LIDAR data. © 2017, The International Biometric Society.
Comparison of model microbial allocation parameters in soils of varying texture

Science.gov (United States)

Hagerty, S. B.; Slessarev, E.; Schimel, J.

2017-12-01

The soil microbial community decomposes the majority of carbon (C) inputs to the soil. However, not all of this C is respired—rather, a substantial portion of the carbon processed by microbes may remain stored in the soil. The balance between C storage and respiration is controlled by microbial turnover rates and C allocation strategies. These microbial community properties may depend on soil texture, which has the potential to influence both the nature and the fate of microbial necromass and extracellular products. To evaluate the role of texture on microbial turnover and C allocation, we sampled four soils from the University of California's Hastings Reserve that varied in texture (one silt loam, two sandy loam, and on clay soil), but support similar grassland plant communities. We added 14C- glucose to the soil and measured the concentration of the label in the carbon dioxide (CO2), microbial biomass, and extractable C pools over 7 weeks. The labeled biomass turned over the slowest in the clay soil; the concentration of labeled biomass was more than 1.5 times the concentration of the other soils after 8 weeks. The clay soil also had the lowest mineralization rate of the label, and mineralization slowed after two weeks. In contrast, in the sandier soils mineralization rates were higher and did not plateau until 5 weeks into the incubation period. We fit the 14C data to a microbial allocation model and estimated microbial parameters; assimilation efficiency, exudation, and biomass specific respiration and turnover for each soil. We compare these parameters across the soil texture gradient to assess the extent to which models may need to account for variability in microbial C allocation across soils of different texture. Our results suggest that microbial C turns over more slowly in high-clay soils than in sandy soils, and that C lost from microbial biomass is retained at higher rates in high-clay soils. Accounting for these differences in microbial allocation
Analysis of the importance for the doses of varying parameters in the BIOPATH-program

International Nuclear Information System (INIS)

Bergstroem, U.

1981-01-01

The doses to individuals and populations from water-borne nuclides leaked from a repository have been calculated earlier using the computer program BIOPATH. The turnover of nuclides in the biosphere is thereby simulated by the application of compartment theory. For the dominant nuclides in the disposal an analysis of the importance of varying parameters has been done, to decide how strongly uncertainties in data will affect resulting doses. The essential part has been the transfer coefficients but also the uptake in the food-chains has been studied. The purpose of the study has also been to make proposals for forthcoming efforts to improve the basis for such calculations. The study shows the great importance of the surface water-soil-groundwater-drinking water system for the dose. Thereby the most important question is the solubility of the nuclides in the different water reservoirs. (Auth.)
Exponential synchronization of chaotic systems with time-varying delays and parameter mismatches via intermittent control.

Science.gov (United States)

Cai, Shuiming; Hao, Junjun; Liu, Zengrong

2011-06-01

This paper studies the synchronization of coupled chaotic systems with time-varying delays in the presence of parameter mismatches by means of periodically intermittent control. Some novel and useful quasisynchronization criteria are obtained by using the methods which are different from the techniques employed in the existing works, and the derived results are less conservative. Especially, a strong constraint on the control width that the control width should be larger than the time delay imposed by the current references is released in this paper. Moreover, our results show that the synchronization criteria depend on the ratio of control width to control period, but not the control width or the control period. Finally, some numerical simulations are given to show the effectiveness of the theoretical results.
State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

International Nuclear Information System (INIS)

Lakshmanan, S.; Park, Ju H.; Jung, H. Y.; Balasubramaniam, P.

2012-01-01

This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed time-varying delays and Markovian jumping parameters. The addressed neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov process. By construction of a suitable Lyapunov—Krasovskii functional, a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square. The criterion is formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages
Studies of wheel-running reinforcement: parameters of Herrnstein's (1970) response-strength equation vary with schedule order.

Science.gov (United States)

Belke, T W

2000-05-01

Six male Wistar rats were exposed to different orders of reinforcement schedules to investigate if estimates from Herrnstein's (1970) single-operant matching law equation would vary systematically with schedule order. Reinforcement schedules were arranged in orders of increasing and decreasing reinforcement rate. Subsequently, all rats were exposed to a single reinforcement schedule within a session to determine within-session changes in responding. For each condition, the operant was lever pressing and the reinforcing consequence was the opportunity to run for 15 s. Estimates of k and R(O) were higher when reinforcement schedules were arranged in order of increasing reinforcement rate. Within a session on a single reinforcement schedule, response rates increased between the beginning and the end of a session. A positive correlation between the difference in parameters between schedule orders and the difference in response rates within a session suggests that the within-session change in response rates may be related to the difference in the asymptotes. These results call into question the validity of parameter estimates from Herrnstein's (1970) equation when reinforcer efficacy changes within a session.
The Design and Its Application in Secure Communication and Image Encryption of a New Lorenz-Like System with Varying Parameter

Directory of Open Access Journals (Sweden)

Lilian Huang

2016-01-01

Full Text Available A new Lorenz-like chaotic system with varying parameter is proposed by adding a state feedback function. The structure of the new designed system is simple and has more complex dynamic behaviors. The chaos behavior of the new system is studied by theoretical analysis and numerical simulation. And the bifurcation diagram shows a chaos-cycle-chaos evolution when the new parameter changes. Then a new synchronization scheme by a single state variable drive is given based on the new system and a chaotic parameter modulation digital secure communication system is also constructed. The results of simulation demonstrate that the new proposed system could be well applied in secure communication. Otherwise, based on the new system, the encryption and decryption of image could be achieved also.
Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

Science.gov (United States)

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.
Correlation between pre-treatment quasispecies complexity and treatment outcome in chronic HCV genotype 3a.

LENUS (Irish Health Repository)

Moreau, Isabelle

2012-02-03

Pre-treatment HCV quasispecies complexity and diversity may predict response to interferon based anti-viral therapy. The objective of this study was to retrospectively (1) examine temporal changes in quasispecies prior to the start of therapy and (2) investigate extensively quasispecies evolution in a group of 10 chronically infected patients with genotype 3a, treated with pegylated alpha2a-Interferon and ribavirin. The degree of sequence heterogeneity within the hypervariable region 1 was assessed by analyzing 20-30 individual clones in serial serum samples. Genetic parameters, including amino acid Shannon entropy, Hamming distance and genetic distance were calculated for each sample. Treatment outcome was divided into (1) sustained virological responders (SVR) and (2) treatment failure (TF). Our results indicate, (1) quasispecies complexity and diversity are lower in the SVR group, (2) quasispecies vary temporally and (3) genetic heterogeneity at baseline can be use to predict treatment outcome. We discuss the results from the perspective of replicative homeostasis.
Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

Directory of Open Access Journals (Sweden)

N. Sujay Raghavendra

2015-12-01

Full Text Available This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of both the SVR and WP–SVR models is assessed using different statistical indices. From the comparative result analysis of the developed models, it can be seen that WP–SVR model outperforms the classic SVR model in predicting groundwater levels at all the three well locations (e.g. NRMSE(WP–SVR = 7.14, NRMSE(SVR = 12.27; NSE(WP–SVR = 0.91, NSE(SVR = 0.8 during the test phase with respect to well location at Surathkal. Therefore, using the WP–SVR model is highly acceptable for modeling and forecasting of groundwater level fluctuations.
Changes in liver stiffness measurement using acoustic radiation force impulse elastography after antiviral therapy in patients with chronic hepatitis C.

Directory of Open Access Journals (Sweden)

Sheng-Hung Chen

Full Text Available To compare on-treatment and off-treatment parameters acquired using acoustic radiation force impulse elastography, the Fibrosis-4 (FIB-4 index, and aspartate aminotransferase-to-platelet ratio index (APRI in patients with chronic hepatitis C (CHC.Patients received therapies based on pegylated interferon or direct-acting antiviral agents. The changes in paired patient parameters, including liver stiffness (LS values, the FIB-4 index, and APRI, from baseline to sustained virologic response (SVR visit (24 weeks after the end of treatment were compared. Multiple regression models were used to identify significant factors that explained the correlations with LS, FIB-4, and APRI values and SVR.A total of 256 patients were included, of which 219 (85.5% achieved SVR. The paired LS values declined significantly from baseline to SVR visit in all groups and subgroups except the nonresponder subgroup (n = 10. Body mass index (P = 0.0062 and baseline LS (P < 0.0001 were identified as independent factors that explained the LS declines. Likewise, the baseline FIB-4 (P < 0.0001 and APRI (P < 0.0001 values independently explained the declines in the FIB-4 index and APRI, respectively. Moreover, interleukin-28B polymorphisms, baseline LS, and rapid virologic response were identified as independent correlates with SVR.Paired LS measurements in patients treated for CHC exhibited significant declines comparable to those in FIB-4 and APRI values. These declines may have correlated with the resolution of necroinflammation. Baseline LS values predicted SVR.

Online Energy Management of City Cars with Multi-Objective Linear Parameter-Varying L2-Gain Control

Directory of Open Access Journals (Sweden)

Boe-Shong Hong

2015-09-01

Full Text Available This work aims at online regulating transient current out of the batteries of small-sized electric cars that transport people and goods around cities. In a city with heavy traffic, transient current dominates the energy economy and propulsion capability, which are in opposition to each other. In order to manage the trade-off between energy consumption per distance and propulsion capability in transience, the authors improve on previous work on multi-objective linear parameter-varying (LPV L2-gain control. The observer embedded into this multi-objective controller no longer assumes Kalman-filtering structure, and structural conservatism is thus removed. A full-spectrum set of experiments is performed. The results reveal that the feedback design significantly improves energy-motion management.
Detection of Buried Objects by Means of a SAP Technique: Comparing MUSIC- and SVR-Based Approaches

Science.gov (United States)

Meschino, S.; Pajewski, L.; Pastorino, M.; Randazzo, A.; Schettini, G.

2012-04-01

This work is focused on the application of a Sub-Array Processing (SAP) technique to the detection of metallic cylindrical objects embedded in a dielectric half-space. The identification of buried cables, pipes, conduits, and other cylindrical utilities, is an important problem that has been extensively studied in the last years. Most commonly used approaches are based on the use of electromagnetic sensing: a set of antennas illuminates the ground and the collected echo is analyzed in order to extract information about the scenario and to localize the sought objects [1]. In a SAP approach, algorithms for the estimation of Directions of Arrival (DOAs) are employed [2]: they assume that the sources (in this paper, currents induced on buried targets) are in the far-field region of the receiving array, so that the received wavefront can be considered as planar, and the main angular direction of the field can be estimated. However, in electromagnetic sensing of buried objects, the scatterers are usually quite near to the antennas. Nevertheless, by dividing the whole receiving array in a suitable number of sub-arrays, and by finding a dominant DOA for each one, it is possible to localize objects that are in the far-field of the sub-array, although being in the near-field of the array. The DOAs found by the sub-arrays can be triangulated, obtaining a set of crossings with intersections condensed around object locations. In this work, the performances of two different DOA algorithms are compared. In particular, a MUltiple SIgnal Classification (MUSIC)-type method [3] and Support Vector Regression (SVR) based approach [4] are employed. The results of a Cylindrical-Wave Approach forward solver are used as input data of the detection procedure [5]. To process the crossing pattern, the region of interest is divided in small windows, and a Poisson model is adopted for the statistical distribution of intersections in the windows. Hypothesis testing procedures are used (imposing
PERAMALAN JUMLAH KUNJUNGAN WISATAWAN AUSTRALIA YANG BERKUNJUNG KE BALI MENGGUNAKAN MODEL TIME VARYING PARAMETER (TVP

Directory of Open Access Journals (Sweden)

I PUTU GEDE DIAN GERRY SUWEDAYANA

2016-08-01

Full Text Available The purpose of this research is to forecast the number of Australian tourists arrival to Bali using Time Varying Parameter (TVP model based on inflation of Indonesia and exchange rate AUD to IDR from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is . This model has a value of mean absolute percentage error (MAPE is equal to dan root mean square percentage error (RMSPE is equal to . The number of Australian tourists arrival to Bali for the next five periods is predicted: ; ; ; ; and (January - May 2016.
Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model

International Nuclear Information System (INIS)

Hong, W.-C.

2009-01-01

Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS)
Efficient design of gain-flattened multi-pump Raman fiber amplifiers using least squares support vector regression

Science.gov (United States)

Chen, Jing; Qiu, Xiaojie; Yin, Cunyi; Jiang, Hao

2018-02-01

An efficient method to design the broadband gain-flattened Raman fiber amplifier with multiple pumps is proposed based on least squares support vector regression (LS-SVR). A multi-input multi-output LS-SVR model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. The proposed approach contains two stages: offline training stage and online optimization stage. During the offline stage, the LS-SVR model is trained. Owing to the good generalization capability of LS-SVR, the net gain spectrum can be directly and accurately obtained when inputting any combination of the pump wavelength and power to the well-trained model. During the online stage, we incorporate the LS-SVR model into the particle swarm optimization algorithm to find the optimal pump configuration. The design results demonstrate that the proposed method greatly shortens the computation time and enhances the efficiency of the pump parameter optimization for Raman fiber amplifier design.
Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

International Nuclear Information System (INIS)

Kavaklioglu, Kadir

2011-01-01

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)
Time-Varying FOPDT Modeling and On-line Parameter Identification

DEFF Research Database (Denmark)

Yang, Zhenyu; Sun, Zhen

2013-01-01

on the Mixed-Integer-Nonlinear Programming, Least-Mean-Square and sliding window techniques. The proposed approaches can simultaneously estimate the time-dependent system parameters, as well as the unknown disturbance input if it is the case, in an on-line manner. The proposed concepts and algorithms...
SVR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Severe Thunderstorm Warnings (SVRs) are issued by NWS Weather Forecast Office (WFO) meteorologists when there is radar of satellite indication and/or reliable...
Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

International Nuclear Information System (INIS)

Jordehi, Ahmad Rezaee

2016-01-01

Highlights: • A modified PSO has been proposed for parameter estimation of PV cells and modules. • In the proposed modified PSO, acceleration coefficients are changed during run. • The proposed modified PSO mitigates premature convergence problem. • Parameter estimation problem has been solved for both PV cells and PV modules. • The results show that proposed PSO outperforms other state of the art algorithms. - Abstract: Estimating circuit model parameters of PV cells/modules represents a challenging problem. PV cell/module parameter estimation problem is typically translated into an optimisation problem and is solved by metaheuristic optimisation problems. Particle swarm optimisation (PSO) is considered as a popular and well-established optimisation algorithm. Despite all its advantages, PSO suffers from premature convergence problem meaning that it may get trapped in local optima. Personal and social acceleration coefficients are two control parameters that, due to their effect on explorative and exploitative capabilities, play important roles in computational behavior of PSO. In this paper, in an attempt toward premature convergence mitigation in PSO, its personal acceleration coefficient is decreased during the course of run, while its social acceleration coefficient is increased. In this way, an appropriate tradeoff between explorative and exploitative capabilities of PSO is established during the course of run and premature convergence problem is significantly mitigated. The results vividly show that in parameter estimation of PV cells and modules, the proposed time varying acceleration coefficients PSO (TVACPSO) offers more accurate parameters than conventional PSO, teaching learning-based optimisation (TLBO) algorithm, imperialistic competitive algorithm (ICA), grey wolf optimisation (GWO), water cycle algorithm (WCA), pattern search (PS) and Newton algorithm. For validation of the proposed methodology, parameter estimation has been done both for
Protein pathway activation associated with sustained virologic response in patients with chronic hepatitis C treated with pegylated interferon (PEG-IFN) and ribavirin (RBV).

Science.gov (United States)

Younossi, Zobair M; Limongi, Dolores; Stepanova, Maria; Pierobon, Mariaelena; Afendy, Arian; Mehta, Rohini; Baranova, Ancha; Liotta, Lance; Petricoin, Emanuel

2011-02-04

Only half of chronic hepatitis C (CH-C) patients treated with pegylated interferon and ribavirin (PEG-IFN+RBV) achieve sustained virologic response) SVR. In addition to known factors, we postulated that activation of key protein signaling networks in the peripheral blood mononuclear cells (PBMCs) may contribute to SVR due to inherent patient-specific basal immune cell signaling architecture. In this study, we included 92 patients with CH-C. PBMCs were collected while patients were not receiving treatment and used for phosphoprotein-based network profiling. Patients received a full course of PEG-IFN+RBV with overall SVR of 55%. From PBMC, protein lysates were extracted and then used for Reverse Phase Protein Microarray (RPMA) analysis, which quantitatively measured the levels of cytokines and activation levels of 25 key protein signaling molecules involved in immune cell regulation and interferon alpha signaling. Regression models for predicting SVR were generated by stepwise bidirectional selection. Both clinical-laboratory and RPMA parameters were used as predictor variables. Model accuracies were estimated using 10-fold cross-validation. Our results show that by comparing patients who achieved SVR to those who did not, phosphorylation levels of 6 proteins [AKT(T308), JAK1(Y1022/1023), p70 S6 Kinase (S371), PKC zeta/lambda(T410/403), TYK2(Y1054/1055), ZAP-70(Y319)/Syk(Y352)] and overall levels of 6 unmodified proteins [IL2, IL10, IL4, IL5, TNF-alpha, CD5L] were significantly different (P < 0.05). For SVR, the model based on a combination of clinical and proteome parameters was developed, with an AUC = 0.914, sensitivity of 92.16%, and specificity of 85.0%. This model included the following parameters: viral genotype, previous treatment status, BMI, phosphorylated states of STAT2, AKT, LCK, and TYK2 kinases as well as steady state levels of IL4, IL5, and TNF-alpha. In conclusion, SVR could be predicted by a combination of clinical, cytokine, and protein signaling
Predictive based monitoring of nuclear plant component degradation using support vector regression

International Nuclear Information System (INIS)

Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2015-01-01

Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component's respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.
Application of support vector regression for optimization of vibration flow field of high-density polyethylene melts characterized by small angle light scattering

Science.gov (United States)

Xian, Guangming

2018-03-01

In this paper, the vibration flow field parameters of polymer melts in a visual slit die are optimized by using intelligent algorithm. Experimental small angle light scattering (SALS) patterns are shown to characterize the processing process. In order to capture the scattered light, a polarizer and an analyzer are placed before and after the polymer melts. The results reported in this study are obtained using high-density polyethylene (HDPE) with rotation speed at 28 rpm. In addition, support vector regression (SVR) analytical method is introduced for optimization the parameters of vibration flow field. This work establishes the general applicability of SVR for predicting the optimal parameters of vibration flow field.
On the link between oil price and exchange rate: A time-varying VAR parameter approach

International Nuclear Information System (INIS)

Bremond, Vincent; Razafindrabe, Tovonony; Hache, Emmanuel

2015-07-01

The aim of this paper is to study the relationship between the effective exchange rate of the dollar and the oil price dynamics from 1976 to 2013. In this context, we propose to explore the economic literature dedicated to financial channels factors (exchange rate, monetary policy, and international liquidity) that could affect the oil price dynamics. In addition to oil prices and the effective exchange rate of the dollar, we use the dry cargo index as a proxy for the real economic activity and prices for precious and industrial raw materials. Using a Bayesian time-varying parameter vector auto-regressive estimation, our main results show that the US Dollar effective exchange rate elasticity of the crude oil prices is not constant across the time and remains negative from 1989. It then highlights that a depreciation of the effective exchange rate of the dollar leads to an increase of the crude oil prices. Our paper also demonstrates the growing influence of financial and commodities markets development upon the global economy. (authors)
Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Science.gov (United States)

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.
Pegylated interferon fractal pharmacokinetics: individualized dosing for hepatitis C virus infection.

Science.gov (United States)

Jain, Mamta K; Pasipanodya, Jotam G; Alder, Lara; Lee, William M; Gumbo, Tawanda

2013-03-01

Despite recent advances in hepatitis C virus (HCV) therapeutics, the combination of pegylated interferon and ribavirin (PEGIFN/RBV) remains the cornerstone of treatment. Optimization and individualization of PEGIFN dosing could improve outcomes. Week one PEGIFN serum concentrations in 42 HCV genotype 1-infected patients treated with conventional PEGIFN/RBV were analyzed using multicompartmental pharmacokinetic models. For each patient, pharmacokinetic parameter estimates, weight, age, interleukin-28B (IL-28B) single-nucleotide polymorphism, CD4 count, baseline HCV RNA, gender, race, and HIV status were examined using classification and regression tree analysis to identify factors predictive of sustained viral response (SVR). Survival analysis was performed to compare the time to undetectable viral load in patients with and without the highest scoring predictor. PEGIFN concentrations varied at least 87-fold. Pharmacokinetics were best described by a two-compartment model with an 8.4-h absorption lag. Patient weight correlated with PEGIFN systemic clearance based on fractal geometry relationships. SVR was achieved in 36% of patients; a PEGIFN cumulative 1-week area under the curve (AUC) of ≤0.79 mg · h/liter scored highest in predicting poor response, followed by a weight of ≥93.7 kg. Patients with a PEGIFN AUC of >0.79 mg · h/liter achieved undetectable viral load more rapidly than those with a lower AUC (hazard ratio, 1.63; 95% confidence interval, 1.21 to 2.04). PEGIFN exhibits wide pharmacokinetic variability, mainly driven by patient weight, so that the standard dose may not reach levels needed to achieve SVR. Optimizing dose to patient weight and PEGIFN AUC in the first week offers a solution to improve SVR and to potentially shorten duration of therapy.
Predictors Associated with Increase in Skeletal Muscle Mass after Sustained Virological Response in Chronic Hepatitis C Treated with Direct Acting Antivirals

Directory of Open Access Journals (Sweden)

Kazunori Yoh

2017-10-01

Full Text Available Aims: We aimed to examine changes in skeletal muscle mass in chronic hepatitis C (CHC patients undergoing interferon (IFN-free direct acting antivirals (DAAs therapy who achieved sustained virological response (SVR. Patients and methods: A total of 69 CHC patients treated with DAAs were analyzed. We compared the changes in skeletal muscle index (SMI using bio-impedance analysis at baseline and SMI at SVR. SMI was calculated as the sum of skeletal muscle mass in upper and lower extremities divided by height squared (cm2/m2. Further, we identified pretreatment parameters contributing to the increased SMI at SVR. Results: SMI in males at baseline ranged from 6.73 to 9.08 cm2/m2 (median, 7.65 cm2/m2, while that in females ranged from 4.45 to 7.27 cm2/m2 (median, 5.81 cm2/m2. At SVR, 36 patients (52.2% had increased SMI as compared with baseline. In the univariate analysis, age (p = 0.0392, hyaluronic acid (p = 0.0143, and branched-chain amino acid to tyrosine ratio (BTR (p = 0.0024 were significant pretreatment factors linked to increased SMI at SVR. In the multivariate analysis, only BTR was an independent predictor linked to the increased SMI at SVR (p = 0.0488. Conclusion: Pretreatment BTR level can be helpful for predicting increased SMI after SVR in CHC patients undergoing IFN-free DAAs therapy.
[Extraction Optimization of Rhizome of Curcuma longa by Response Surface Methodology and Support Vector Regression].

Science.gov (United States)

Zhou, Pei-pei; Shan, Jin-feng; Jiang, Jian-lan

2015-12-01

To optimize the optimal microwave-assisted extraction method of curcuminoids from Curcuma longa. On the base of single factor experiment, the ethanol concentration, the ratio of liquid to solid and the microwave time were selected for further optimization. Support Vector Regression (SVR) and Central Composite Design-Response Surface Methodology (CCD) algorithm were utilized to design and establish models respectively, while Particle Swarm Optimization (PSO) was introduced to optimize the parameters of SVR models and to search optimal points of models. The evaluation indicator, the sum of curcumin, demethoxycurcumin and bisdemethoxycurcumin by HPLC, were used. The optimal parameters of microwave-assisted extraction were as follows: ethanol concentration of 69%, ratio of liquid to solid of 21 : 1, microwave time of 55 s. On those conditions, the sum of three curcuminoids was 28.97 mg/g (per gram of rhizomes powder). Both the CCD model and the SVR model were credible, for they have predicted the similar process condition and the deviation of yield were less than 1.2%.
An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study

International Nuclear Information System (INIS)

Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Saboohi, Hadi; Abdul Wahab, Ainuddin Wahid; Protić, Milan; Zalnezhad, Erfan; Mirhashemi, Seyed Mohammad Amin

2014-01-01

Highlights: • Probabilistic distribution functions of wind speed. • Two parameter Weibull probability distribution. • To build an effective prediction model of distribution of wind speed. • Support vector regression application as probability function for wind speed. - Abstract: The probabilistic distribution of wind speed is among the more significant wind characteristics in examining wind energy potential and the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can be easily obtained. Therefore, the probability distribution of wind speed is a very important piece of information required in assessing wind energy potential. For this reason, a large number of studies have been established concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Although the two-parameter Weibull distribution comprises a widely used and accepted method, solving the function is very challenging. In this study, the polynomial and radial basis functions (RBF) are applied as the kernel function of support vector regression (SVR) to estimate two parameters of the Weibull distribution function according to previously established analytical methods. Rather than minimizing the observed training error, SVR p oly and SVR r bf attempt to minimize the generalization error bound, so as to achieve generalized performance. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved using the SVR approach compared to other soft computing methodologies
Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

Science.gov (United States)

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

Directory of Open Access Journals (Sweden)

Razana Alwee

2013-01-01

Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Optimal critic learning for robot control in time-varying environments.

Science.gov (United States)

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.
Magnetized strange quark matter in f(R, T) gravity with bilinear and special form of time varying deceleration parameter

Science.gov (United States)

Sahoo, P. K.; Sahoo, Parbati; Bishi, Binaya K.; Aygün, Sezgin

2018-04-01

In this paper, we have studied homogeneous and anisotropic locally rotationally symmetric (LRS) Bianchi type-I model with magnetized strange quark matter (MSQM) distribution and cosmological constant Λ in f(R, T) gravity where R is the Ricci scalar and T the trace of matter source. The exact solutions of the field equations are obtained under bilinear and special form of time varying deceleration parameter (DP). Firstly, we have considered two specific forms of bilinear DP with a single parameter of the form: q = α(1-t)/1+t and q = -αt/1+t, which leads to the constant or linear nature of the function based on the constant α. Second one is the special form of the DP as q = - 1 + β/1+aβ. From the results obtained here, one can observe that in the early universe magnetic flux has more effects and it reduces gradually in the later stage. For t → ∞, we get p → -Bc and ρ → Bc. The behaviour of strange quark matter along with magnetic epoch gives an idea of accelerated expansion of the universe as per the observations of the type Ia Supernovae.
Effect of Varying Controller Parameters on the Performance of a ...

African Journals Online (AJOL)

This paper presents the results of computer simulation studies designed to isolate the effects of the major parameters of a fuzzy logic controller namely the range of the universe of discourse, the extent of overlap of the fuzzy sets, the rules in the rule base and the modes of the output fuzzy sets on the performance of a fuzzy ...
Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

Science.gov (United States)

Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

2017-01-01

A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.
Analysis and design of composite slab by varying different parameters

Science.gov (United States)

Lambe, Kedar; Siddh, Sharda

2018-03-01

Composite deck slabs are in demand because of its faster, lighter and economical construction work. Composite slab consists of cold formed deck profiled sheet and concrete either lightweight or normal. Investigation of shear behaviour of the composite slab is very complex. Shear bond strength depends on the various parameter such as a shape of sheeting, a thickness of the sheet, type of embossment and its frequency of use, shear stiffener or intermediate stiffener, type of load, an arrangement of load, length of shear span, the thickness of concrete and support friction etc. In present study finite element analysis is carried out with ABAQUS 6.13, a simply supported composite slab is considered for the investigation of the shear bond behaviour of the composite slab by considering variation in three different parameters, the shape of a sheet, thickness of sheet and shear span. Different shear spans of two different shape of cold formed deck profiled sheet i.e. with intermediate stiffeners and without intermediate stiffeners are considered with two different thicknesses (0.8 mm and 1.2 mm) for simulation. In present work, simulation of models has done for static loading with 20 mm mesh size is considered.
Epidemiological characteristics and response to peginterferon plus ribavirin treatment of hepatitis C virus genotype 4 infection.

Science.gov (United States)

Roulot, D; Bourcier, V; Grando, V; Deny, P; Baazia, Y; Fontaine, H; Bailly, F; Castera, L; De Ledinghen, V; Marcellin, P; Poupon, R; Bourlière, M; Zarski, J P; Roudot-Thoraval, F

2007-07-01

Hepatitis C virus genotype 4 (HCV-4) infection is progressing in Europe, where epidemiology and sustained virological response (SVR) seem to be different than in the Middle East. We analysed epidemiological features and SVR rates in a retrospective study of 1532 HCV-4-infected patients, including 1056 patients infected in France, 227 immigrants infected in Egypt and 249 in sub-Saharan Africa. SVR rates were assessed in 242 naive patients of the 1532, who received peginterferon plus ribavirin for 48 weeks. HCV subtype 4a or 4d was the most common among patients infected in France, where the predominant route of transmission was intravenous drug abuse. The 4a subtype was largely predominant (93%) among patients infected in Egypt, where transmission was mostly because of parenteral treatment for schistosomiasis. More than seven different subtypes and no predominant route of infection were found in patients infected in sub-Saharan Africa. Liver fibrosis was significantly less severe in patients infected in France and Africa than in patients infected in Egypt. SVR rates were higher in patients infected in Egypt, compared with those infected in France or Africa (54.9%, 40.3% and 32.4%, respectively, P < 0.05). An overall better response was observed in patients infected with the 4a subtype. In multivariate analysis, two factors were associated independently with SVR: the Egyptian origin of transmission and the absence of severe fibrosis. In conclusion, the distribution of HCV-4 subtypes varies with the geographical origin of transmission and affects the SVR following antiviral treatment.
Boceprevir: a protease inhibitor for the treatment of hepatitis C.

Science.gov (United States)

Chang, Mei H; Gordon, Lori A; Fung, Horatio B

2012-10-01

Boceprevir is a protease inhibitor indicated for the treatment of chronic hepatitis C virus (HCV) genotype 1 infection in combination with peginterferon and ribavirin for treatment-naive patients and those who previously failed to improve with interferon and ribavirin treatment. This article provides an overview of the mechanism of action, pharmacologic and pharmacokinetic properties, clinical efficacy, and tolerability of boceprevir. Relevant information was identified through a search of PubMed (1990-July 2012), EMBASE (1990-July 2012), International Pharmaceutical Abstracts (1970-July 2012), and Google Scholar using the key words boceprevir, SCH 503034, non-structural protein 3 (NS3) serine protease inhibitor, and direct-acting antiviral agent (DAA). Additional information was obtained from the US Food and Drug Administration's Web site, review of the reference lists of identified articles, and posters and abstracts from scientific meetings. Clinical efficacy of boceprevir was assessed in 2 Phase III trials, Serine Protease Inhibitor Therapy-2 (SPRINT-2) for treatment-naive patients and Retreatment with HCV Serine Protease Inhibitor Boceprevir and PegIntron/Rebetol 2 (RESPOND-2) for treatment-experienced patients. In SPRINT-2, patients were randomized to receive peginterferon + ribavirin (PR) or peginterferon + ribavirin + boceprevir (PRB); duration of boceprevir therapy varied from 24, 32, to 44 weeks on the basis of HCV RNA results. The primary endpoint was achievement of sustained virologic response (SVR; lower limit of detection, 9.3 IU/mL). The addition of boceprevir was shown to be superior, with overall SVR rates ranging from 63% to 66% compared with 38% with PR (P < 0.001). Results of SVR in SPRINT-2 were also reorganized to monitor SVRs in black and non-black patients. Treatment-experienced patients were assessed in RESPOND-2; however, null responders were excluded. Patients were again randomized to PR or PRB; duration of boceprevir therapy varied from
A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Yongquan Dong

2018-04-01

Full Text Available Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence. Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia and the New York Independent System Operator (NYISO, NY, USA, show that the proposed SSVRCCS model outperforms other alternative models.
Modelling how reversal of immune exhaustion elicits cure of chronic hepatitis C after the end of treatment with direct-acting antiviral agents.

Science.gov (United States)

Baral, Subhasish; Roy, Rahul; Dixit, Narendra M

2018-05-09

A fraction of chronic hepatitis C patients treated with direct-acting antivirals (DAAs) achieved sustained virological responses (SVR), or cure, despite having detectable viremia at the end of treatment (EOT). This observation, termed EOT + /SVR, remains puzzling and precludes rational optimization of treatment durations. One hypothesis to explain EOT + /SVR, the immunologic hypothesis, argues that the viral decline induced by DAAs during treatment reverses the exhaustion of cytotoxic T lymphocytes (CTLs), which then clear the infection after treatment. Whether the hypothesis is consistent with data of viral load changes in patients who experienced EOT + /SVR is unknown. Here, we constructed a mathematical model of viral kinetics incorporating the immunologic hypothesis and compared its predictions with patient data. We found the predictions to be in quantitative agreement with patient data. Using the model, we unraveled an underlying bistability that gives rise to EOT + /SVR and presents a new avenue to optimize treatment durations. Infected cells trigger both activation and exhaustion of CTLs. CTLs in turn kill infected cells. Due to these competing interactions, two stable steady states, chronic infection and viral clearance, emerge, separated by an unstable steady state with intermediate viremia. When treatment during chronic infection drives viremia sufficiently below the unstable state, spontaneous viral clearance results post-treatment, marking EOT + /SVR. The duration to achieve this desired reduction in viremia defines the minimum treatment duration required for ensuring SVR, which our model can quantify. Estimating parameters defining the CTL response of individuals to HCV infection would enable the application of our model to personalize treatment durations. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Enhancing damping of gas bearings using linear parameter-varying control

DEFF Research Database (Denmark)

Theisen, Lukas Roy Svane; Niemann, Hans Henrik; Galeazzi, Roberto

2017-01-01

systems to regulate the injection pressure of the fluid. Due to the strong dependencies of system performance on system parameters, the sought controller should be robust over a large range of operational conditions. This paper addresses the damping enhancement of controllable gas bearings through robust...... control approaches. Through an extensive experimental campaign the paper evaluates two robust controllers, a linear parametervarying (LPV) controller and ∞ controller, on their capability to guarantee stability and performance of a gas bearing across the large operational envelopes in rotational speed...
A New Method of 3D Facial Expression Animation

Directory of Open Access Journals (Sweden)

Shuo Sun

2014-01-01

Full Text Available Animating expressive facial animation is a very challenging topic within the graphics community. In this paper, we introduce a novel ERI (expression ratio image driving framework based on SVR and MPEG-4 for automatic 3D facial expression animation. Through using the method of support vector regression (SVR, the framework can learn and forecast the regression relationship between the facial animation parameters (FAPs and the parameters of expression ratio image. Firstly, we build a 3D face animation system driven by FAP. Secondly, through using the method of principle component analysis (PCA, we generate the parameter sets of eigen-ERI space, which will rebuild reasonable expression ratio image. Then we learn a model with the support vector regression mapping, and facial animation parameters can be synthesized quickly with the parameters of eigen-ERI. Finally, we implement our 3D face animation system driving by the result of FAP and it works effectively.
Linear parameter-varying modeling and control of the steam temperature in a Canadian SCWR

Energy Technology Data Exchange (ETDEWEB)

Sun, Peiwei, E-mail: sunpeiwei@mail.xjtu.edu.cn; Zhang, Jianmin; Su, Guanghui

2017-03-15

Highlights: • Nonlinearity of Canadian SCWR is analyzed based on step responses and Nyquist plots. • LPV model is derived through Jacobian linearization and curve fitting. • An output feedback H{sub ∞} controller is synthesized for the steam temperature. • The control performance is evaluated by step disturbances and wide range operation. • The controller can stabilize the system and reject the reactor power disturbance. - Abstract: The Canadian direct-cycle Supercritical Water-cooled Reactor (SCWR) is a pressure-tube type SCWR under development in Canada. The dynamics of the steam temperature have a high degree of nonlinearity and are highly sensitive to reactor power disturbances. Traditional gain scheduling control cannot theoretically guarantee stability for all operating regions. The control performance can also be deteriorated when the controllers are switched. In this paper, a linear parameter-varying (LPV) strategy is proposed to solve such problems. Jacobian linearization and curve fitting are applied to derive the LPV model, which is verified using a nonlinear dynamic model and determined to be sufficiently accurate for control studies. An output feedback H{sub ∞} controller is synthesized to stabilize the steam temperature system and reject reactor power disturbances. The LPV steam temperature controller is implemented using a nonlinear dynamic model, and step changes in the setpoints and typical load patterns are carried out in the testing process. It is demonstrated through numerical simulation that the LPV controller not only stabilizes the steam temperature under different disturbances but also efficiently rejects reactor power disturbances and suppresses the steam temperature variation at different power levels. The LPV approach is effective in solving control problems of the steam temperature in the Canadian SCWR.
Comparison of Campylobacter jejuni isolates from human, food, veterinary and environmental sources in Iceland using PFGE, MLST and fla-SVR sequencing.

Science.gov (United States)

Magnússon, S H; Guðmundsdóttir, S; Reynisson, E; Rúnarsson, A R; Harðardóttir, H; Gunnarson, E; Georgsson, F; Reiersen, J; Marteinsson, V Th

2011-10-01

Campylobacter jejuni isolates from various sources in Iceland were genotyped with the aim of assessing the genetic diversity, population structure, source distribution and campylobacter transmission routes to humans. A collection of 584 Campylobacter isolates were collected from clinical cases, food, animals and environment in Iceland in 1999-2002, during a period of national Campylobacter epidemic in Iceland. All isolates were characterized by pulse field gel electrophoresis (PFGE), and selected subset of 52 isolates representing the diversity of the identified PFGE types was further genotyped using multilocus sequence typing (MLST) and fla-SVR sequencing to gain better insight into the population structure. The results show a substantial diversity within the Icelandic Campylobacter population. Majority of the human Campylobacter infections originated from domestic chicken and cattle isolates. MLST showed the isolates to be distributed among previously reported and common sequence type complexes in the MLST database. The genotyping of Campylobacter from various sources has not previously been reported from Iceland, and the results of the study gave a valuable insight into the population structure of Camp. jejuni in Iceland, source distribution and transmission routes to humans. The geographical isolation of Iceland in the north Atlantic provides new information on Campylobacter population dynamics on a global scale. Journal of Applied Microbiology © 2011 The Society for Applied Microbiology No claim to Icelandic Government works.
Artificial neural network modeling of DDGS flowability with varying process and storage parameters

Science.gov (United States)

Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....
Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies--a comparative study.

Science.gov (United States)

Zakaria, Rozalina; Sheng, Ong Yong; Wern, Kam; Shamshirband, Shahaboddin; Wahab, Ainuddin Wahid Abdul; Petković, Dalibor; Saboohi, Hadi

2014-05-01

A soft methodology study has been applied on tapered plastic multimode sensors. This study basically used tapered plastic multimode fiber [polymethyl methacrylate (PMMA)] optics as a sensor. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using a calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at high concentrations. As the concentration was varied from 0 to 6 ppm, the output voltage of the sensor increased linearly. The silver film coating increased the sensitivity of the proposed sensor because of the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. In this study, the polynomial and radial basis function (RBF) were applied as the kernel function of the support vector regression (SVR) to estimate and predict the output voltage response of the sensors with and without silver film according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf were used in an attempt to minimize the generalization error bound so as to achieve generalized performance. An adaptive neuro-fuzzy interference system (ANFIS) approach was also investigated for comparison. The experimental results showed that improvements in the predictive accuracy and capacity for generalization can be achieved by the SVR_poly approach in comparison to the SVR_rbf methodology. The same testing errors were found for the SVR_poly approach and the ANFIS approach.
Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks

Science.gov (United States)

Zaal, Peter M. T.; Sweet, Barbara T.

2012-01-01

Recent developments in fly-by-wire control architectures for rotorcraft have introduced new interest in the identification of time-varying pilot control behavior in multi-axis control tasks. In this paper a maximum likelihood estimation method is used to estimate the parameters of a pilot model with time-dependent sigmoid functions to characterize time-varying human control behavior. An experiment was performed by 9 general aviation pilots who had to perform a simultaneous roll and pitch control task with time-varying aircraft dynamics. In 8 different conditions, the axis containing the time-varying dynamics and the growth factor of the dynamics were varied, allowing for an analysis of the performance of the estimation method when estimating time-dependent parameter functions. In addition, a detailed analysis of pilots adaptation to the time-varying aircraft dynamics in both the roll and pitch axes could be performed. Pilot control behavior in both axes was significantly affected by the time-varying aircraft dynamics in roll and pitch, and by the growth factor. The main effect was found in the axis that contained the time-varying dynamics. However, pilot control behavior also changed over time in the axis not containing the time-varying aircraft dynamics. This indicates that some cross coupling exists in the perception and control processes between the roll and pitch axes.
An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach

International Nuclear Information System (INIS)

Ozturk, Ilhan; Arisoy, Ibrahim

2016-01-01

The aim of this study is to model crude oil import demand and estimate the price and income elasticities of imported crude oil in Turkey based on a time-varying parameters (TVP) approach with the aim of obtaining accurate and more robust estimates of price and income elasticities. This study employs annual time series data of domestic oil consumption, real GDP, and oil price for the period 1966–2012. The empirical results indicate that both the income and price elasticities are in line with the theoretical expectations. However, the income elasticity is statistically significant while the price elasticity is statistically insignificant. The relatively high value of income elasticity (1.182) from this study suggests that crude oil import in Turkey is more responsive to changes in income level. This result indicates that imported crude oil is a normal good and rising income levels will foster higher consumption of oil based equipments, vehicles and services by economic agents. The estimated income elasticity of 1.182 suggests that imported crude oil consumption grows at a higher rate than income. This in turn reduces oil intensity over time. Therefore, crude oil import during the estimation period is substantially driven by income. - Highlights: • We estimated the price and income elasticities of imported crude oil in Turkey. • Income elasticity is statistically significant and it is 1.182. • The price elasticity is statistically insignificant. • Crude oil import in Turkey is more responsive to changes in income level. • Crude oil import during the estimation period is substantially driven by income.
Water cut measurement of oil–water flow in vertical well by combining total flow rate and the response of a conductance probe

International Nuclear Information System (INIS)

Chen, Jianjun; Xu, Lijun; Cao, Zhang; Zhang, Wen; Liu, Xingbin; Hu, Jinhai

2015-01-01

In this paper, a conductance probe-based well logging instrument was developed and the total flow rate is combined with the response of the conductance probe to estimate the water cut of the oil–water flow in a vertical well. The conductance probe records the time-varying electrical characteristics of the oil–water flow. Linear least squares regression (LSR) and nonlinear support vector regression (SVR) were used to establish models to map the total flow rate and features extracted from the probe response onto the water cut, respectively. Principal component analysis (PCA) and partial least squares analysis (PLSA) techniques were employed to reduce data redundancy within the extracted features. An experiment was carried out in a vertical pipe with an inner diameter of 125 mm and a height of 24 m in an experimental multi-phase flow setup, Daqing Oilfield, China. In the experiment, oil–water flow was used and the total flow rate varied from 10 to 200 m 3 per day and the water cut varied from 0% to 100%. As a direct comparison, the cases were also studied when the total flow rate was not used as an independent input to the models. The results obtained demonstrate that: (1) the addition of the total flow rate as an input to the regression models can greatly improve the accuracy of water cut prediction, (2) the nonlinear SVR model performs much better than the linear LSR model, and (3) for the SVR model with the total flow rate as an input, the adoption of PCA or PLSA not only decreases the dimensions of inputs, but also increases prediction accuracy. The SVR model with five PCA-treated features plus the total flow rate achieves the best performance in water cut prediction, with a coefficient of determination (R 2 ) as high as 0.9970. The corresponding root mean squared error (RMSE) and mean quoted error (MQE) are 0.0312% and 1.99%, respectively. (paper)
Output-only cyclo-stationary linear-parameter time-varying stochastic subspace identification method for rotating machinery and spinning structures

Science.gov (United States)

Velazquez, Antonio; Swartz, R. Andrew

2015-02-01

stochastic subspace identification (SSI) and linear parameter time-varying (LPTV) techniques. Structural response is assumed to be stationary ambient excitation produced by a Gaussian (white) noise within the operative range bandwidth of the machinery or structure in study. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to identify frequencies and complex-valued mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment. A numerical example is carried out based a spinning finite element (SFE) model, and verified using ANSYS® Ver. 12. Finally, comments and observations are provided on how this subspace realization technique can be extended to the problem of modal-parameter identification using only ambient vibration data.
Biochemical parameters in the blood of grass snakes (Natrix natrix in ecosystems under varying degrees of anthropogenic influence

Directory of Open Access Journals (Sweden)

V. Y. Gasso

2016-09-01

Full Text Available The grass snake Natrix natrix (Linnaeus, 1758 is a partly hygrophilous species, distributed throughoutUkraine. This snake may be considered as a test object for environmental biomonitoring. Modern biochemical methods make it possible to obtain new scientific data on the effects of anthropogenic pressure on reptiles. Blood is a sensitive and informative indicator of the condition of an organism as it responds quickly to most changes in exogenous and endogenous factors, and reflects negative influences on both individual and, indirectly, populations. Changes in biochemical parameters may be used as biomarkers of the state of health of reptiles in ecosystems under varying degrees of anthropogenic pressure. Due the increase in anthropogenic influence the development and introduction of new methods of perceptual research, collection of up-to-date information and development of a database of reptile biochemical parameters have become an urgent priority. We collected mature individuals of the grass snake in floodplain ecosystems on the right bank of the Dnieper River in Dnipropetrovsk city. Grass snakes from floodplain habitats on the left bank of theSamaraRiver (O.L. Belgard Prysamarskii International Biosphere Station, Novomoskovsk district, Dnipropetrovsk province were studied as the control specimens. Our study demonstrated statistically significant differences between snakes from the study sites in the amount of albumin, urea and urea nitrogen, and inorganic phosphorus, as well as in alanine aminotransferase (ALT and alkaline phosphatise (AP activity. The amount of albumin in the blood serum of specimens from the anthropogenically transformed areas was significantly lower (by 25% than in that of the snakes caught in the control habitats. Decrease of the albumin concentration usually indicates abnormal processes in the kidneys and liver. According to the changes observed in the concentration of albumin, a corresponding increase in the albumin to

Uncertainty Quantification and Regional Sensitivity Analysis of Snow-related Parameters in the Canadian LAnd Surface Scheme (CLASS)

Science.gov (United States)

Badawy, B.; Fletcher, C. G.

2017-12-01

The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.
Failure prognostics by support vector regression of time series data under stationary/nonstationary environmental and operational conditions

International Nuclear Information System (INIS)

Liu, Jie

2015-01-01

This Ph. D. work is motivated by the possibility of monitoring the conditions of components of energy systems for their extended and safe use, under proper practice of operation and adequate policies of maintenance. The aim is to develop a Support Vector Regression (SVR)-based framework for predicting time series data under stationary/nonstationary environmental and operational conditions. Single SVR and SVR-based ensemble approaches are developed to tackle the prediction problem based on both small and large datasets. Strategies are proposed for adaptively updating the single SVR and SVR-based ensemble models in the existence of pattern drifts. Comparisons with other online learning approaches for kernel-based modelling are provided with reference to time series data from a critical component in Nuclear Power Plants (NPPs) provided by Electricite de France (EDF). The results show that the proposed approaches achieve comparable prediction results, considering the Mean Squared Error (MSE) and Mean Relative Error (MRE), in much less computation time. Furthermore, by analyzing the geometrical meaning of the Feature Vector Selection (FVS) method proposed in the literature, a novel geometrically interpretable kernel method, named Reduced Rank Kernel Ridge Regression-II (RRKRR-II), is proposed to describe the linear relations between a predicted value and the predicted values of the Feature Vectors (FVs) selected by FVS. Comparisons with several kernel methods on a number of public datasets prove the good prediction accuracy and the easy-of-tuning of the hyper-parameters of RRKRR-II. (author)
Power System Event Ranking Using a New Linear Parameter-Varying Modeling with a Wide Area Measurement System-Based Approach

Directory of Open Access Journals (Sweden)

Mohammad Bagher Abolhasani Jabali

2017-07-01

Full Text Available Detecting critical power system events for Dynamic Security Assessment (DSA is required for reliability improvement. The approach proposed in this paper investigates the effects of events on dynamic behavior during nonlinear system response while common approaches use steady-state conditions after events. This paper presents some new and enhanced indices for event ranking based on time-domain simulation and polytopic linear parameter-varying (LPV modeling of a power system. In the proposed approach, a polytopic LPV representation is generated via linearization about some points of the nonlinear dynamic behavior of power system using wide-area measurement system (WAMS concepts and then event ranking is done based on the frequency response of the system models on the vertices. Therefore, the nonlinear behaviors of the system in the time of fault occurrence are considered for events ranking. The proposed algorithm is applied to a power system using nonlinear simulation. The comparison of the results especially in different fault conditions shows the advantages of the proposed approach and indices.
Interferon-γ-inducible protein-10 in chronic hepatitis C: Correlations with insulin resistance, histological features & sustained virological response.

Science.gov (United States)

Crisan, Dana; Grigorescu, Mircea Dan; Radu, Corina; Suciu, Alina; Grigorescu, Mircea

2017-04-01

One of the multiple factors contributing to virological response in chronic hepatitis C (CHC) is interferon-gamma-inducible protein-10 (IP-10). Its level reflects the status of interferon-stimulated genes, which in turn is associated with virological response to antiviral therapy. The aim of this study was to evaluate the role of serum IP-10 levels on sustained virological response (SVR) and the association of this parameter with insulin resistance (IR) and liver histology. Two hundred and three consecutive biopsy proven CHC patients were included in the study. Serum levels of IP-10 were determined using ELISA method. IR was evaluated by homeostasis model assessment-IR (HOMA-IR). Histological features were assessed invasively by liver biopsy and noninvasively using FibroTest, ActiTest and SteatoTest. Predictive factors for SVR and their interrelations were assessed. A cut-off value for IP-10 of 392 pg/ml was obtained to discriminate between responders and non-responders. SVR was obtained in 107 patients (52.70%). Area under the receiver operating characteristic curve for SVR was 0.875 with a sensitivity of 91.6 per cent, specificity 74.7 per cent, positive predictive value 80.3 per cent and negative predictive value 88.7 per cent. Higher values of IP-10 were associated with increasing stages of fibrosis (P<0.01) and higher grades of inflammation (P=0.02, P=0.07) assessed morphologically and noninvasively through FibroTest and ActiTest. Significant steatosis and IR were also associated with increased levels of IP-10 (P=0.01 and P=0.02). In multivariate analysis, IP-10 levels and fibrosis stages were independently associated with SVR. Our findings showed that the assessment of serum IP-10 level could be a predictive factor for SVR and it was associated with fibrosis, necroinflammatory activity, significant steatosis and IR in patients with chronic HCV infection.
Benefit of hepatitis C virus core antigen assay in prediction of therapeutic response to interferon and ribavirin combination therapy.

Science.gov (United States)

Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

2005-01-01

A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay.
Fatty liver in hepatitis C patients post-sustained virological response with direct-acting antivirals.

Science.gov (United States)

Noureddin, Mazen; Wong, Micaela M; Todo, Tsuyoshi; Lu, Shelly C; Sanyal, Arun J; Mena, Edward A

2018-03-21

To determine steatosis and fibrosis prevalence in hepatitis C patients after a sustained virological response achieved with direct-acting antivirals. Transient elastography with controlled attenuation parameter (CAP) was used to assess hepatic steatosis post-sustained virological response (SVR); the CAP technology was not available in the United States at study initiation. Liver stiffness/fibrosis was measured before and 47 wk after treatment completion. Patients with genotype 3 and patients with cirrhosis were excluded. One hundred and one patients were included in the study. Post-SVR there were decreases from baseline in alanine aminotransferase (ALT) (63.1 to 17.8 U/L), aspartate aminotransferase (51.8 to 21.5 U/L) and fibrosis score (7.4 to 6.1 kPa) ( P < 0.05). Post-SVR, 48 patients (47.5%) had steatosis on CAP; of these, 6.25% had advanced fibrosis. Patients with steatosis had higher body mass index (29.0 vs 26.1 kg/m 2 ), glucose (107.8 vs 96.6 mg/dL), ALT (20.4 vs 15.3 mg/dL), CAP score (296.3 vs 212.4 dB/m) and fibrosis score (7.0 vs 5.3 kPa); P < 0.05. Interestingly, compared to baseline, both patients with and without steatosis had change in fibrosis score post-SVR (7.7 kPa vs 7.0 kPa and 7.0 kPa vs 5.3 kPa); alternatively, ( P < 0.05) and therefore patients with steatosis continued to have clinically significant stiffness (≥ 7 kPa). Fatty liver is very common in hepatitis C virus (HCV) patients post-SVR. These patients continue to have elevated mean fibrosis score (≥ 7 kPa) compared to those without fatty liver; some have advanced fibrosis. Long term follow up is needed to assess steatosis and fibrosis in HCV patients post-SVR.
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm

KAUST Repository

Jin, Ick Hoon

2013-10-01

The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Constructing Regional Groundwater Models from Geophysical Data of Varying Type, Age, and Quality

DEFF Research Database (Denmark)

Vest Christiansen, Anders; Auken, Esben; Marker, Pernille Aabye

for parameterization of a 3D model of the subsurface, integrating lithological information from boreholes with resistivity models. The objective is to create a direct input to regional groundwater models for sedimentary areas, where the sand/clay distribution governs the groundwater flow. The resistivity input is all......-inclusive in the sense that we include data from a variety of instruments (DC and EM, ground-based and airborne), with a varying spatial density and varying ages and quality. The coupling between hydrological and geophysical parameters is managed using a translator function with spatially variable parameters, which...
Identification of time-varying structural dynamic systems - An artificial intelligence approach

Science.gov (United States)

Glass, B. J.; Hanagud, S.

1992-01-01

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.
Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

Directory of Open Access Journals (Sweden)

Morufu Olusola Ibitoye

2016-07-01

Full Text Available The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70% and testing (30% subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2 between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
An integrative time-varying frequency detection and channel sounding method for dynamic plasma sheath

Science.gov (United States)

Shi, Lei; Yao, Bo; Zhao, Lei; Liu, Xiaotong; Yang, Min; Liu, Yanming

2018-01-01

The plasma sheath-surrounded hypersonic vehicle is a dynamic and time-varying medium and it is almost impossible to calculate time-varying physical parameters directly. The in-fight detection of the time-varying degree is important to understand the dynamic nature of the physical parameters and their effect on re-entry communication. In this paper, a constant envelope zero autocorrelation (CAZAC) sequence based on time-varying frequency detection and channel sounding method is proposed to detect the plasma sheath electronic density time-varying property and wireless channel characteristic. The proposed method utilizes the CAZAC sequence, which has excellent autocorrelation and spread gain characteristics, to realize dynamic time-varying detection/channel sounding under low signal-to-noise ratio in the plasma sheath environment. Theoretical simulation under a typical time-varying radio channel shows that the proposed method is capable of detecting time-variation frequency up to 200 kHz and can trace the channel amplitude and phase in the time domain well under -10 dB. Experimental results conducted in the RF modulation discharge plasma device verified the time variation detection ability in practical dynamic plasma sheath. Meanwhile, nonlinear phenomenon of dynamic plasma sheath on communication signal is observed thorough channel sounding result.
Marching on in anything: solving electromagnetic field equations with a varying physical parameter

NARCIS (Netherlands)

Tijhuis, A.G.; Zwamborn, A.P.M.; Smith, P.D.; Cloude, S.R.

2002-01-01

In this paper, we consider the determination of electromagnetic fields for a (large) number of values of a physical parameter. We restrict ourselves to the case where the linear system originates from one or more integral equations. We apply an iterative procedure based on the minimization of an
Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries.

Science.gov (United States)

Li, Liwei; Wang, Bo; Meroueh, Samy O

2011-09-26

The community structure-activity resource (CSAR) data sets are used to develop and test a support vector machine-based scoring function in regression mode (SVR). Two scoring functions (SVR-KB and SVR-EP) are derived with the objective of reproducing the trend of the experimental binding affinities provided within the two CSAR data sets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physicochemical properties. SVR-KB and SVR-EP were compared to seven other widely used scoring functions, including Glide, X-score, GoldScore, ChemScore, Vina, Dock, and PMF. Results showed that SVR-KB trained with features obtained from three-dimensional complexes of the PDBbind data set outperformed all other scoring functions, including best performing X-score, by nearly 0.1 using three correlation coefficients, namely Pearson, Spearman, and Kendall. It was interesting that higher performance in rank ordering did not translate into greater enrichment in virtual screening assessed using the 40 targets of the Directory of Useful Decoys (DUD). To remedy this situation, a variant of SVR-KB (SVR-KBD) was developed by following a target-specific tailoring strategy that we had previously employed to derive SVM-SP. SVR-KBD showed a much higher enrichment, outperforming all other scoring functions tested, and was comparable in performance to our previously derived scoring function SVM-SP.
Synchronization of uncertain time-varying network based on sliding mode control technique

Science.gov (United States)

Lü, Ling; Li, Chengren; Bai, Suyuan; Li, Gang; Rong, Tingting; Gao, Yan; Yan, Zhe

2017-09-01

We research synchronization of uncertain time-varying network based on sliding mode control technique. The sliding mode control technique is first modified so that it can be applied to network synchronization. Further, by choosing the appropriate sliding surface, the identification law of uncertain parameter, the adaptive law of the time-varying coupling matrix element and the control input of network are designed, it is sure that the uncertain time-varying network can synchronize effectively the synchronization target. At last, we perform some numerical simulations to demonstrate the effectiveness of the proposed results.
High Frequency Asymptotic Methods for Traveltimes and Anisotropy Parameter Estimation in Azimuthally Varying Media

KAUST Repository

Masmoudi, Nabil

2014-05-01

Traveltimes are conventionally evaluated by solving the zero-order approximation of the Wentzel, Kramers and Brillouin (WKB) expansion of the wave equation. This high frequency approximation is good enough for most imaging applications and provides us with a traveltime equation called the eikonal equation. The eikonal equation is a non-linear partial differential equation which can be solved by any of the familiar numerical methods. Among the most popular of these methods is the method of characteristics which yields the ray tracing equations and the finite difference approaches. In the first part of the Master Thesis, we use the ray tracing method to solve the eikonal equation to get P-waves traveltimes for orthorhombic models with arbitrary orientation of symmetry planes. We start with a ray tracing procedure specified in curvilinear coordinate system valid for anisotropy of arbitrary symmetry. The coordinate system is constructed so that the coordinate lines are perpendicular to the symmetry planes of an orthorohombic medium. Advantages of this approach are the conservation of orthorhombic symmetry throughout the model and reduction of the number of parameters specifying the model. We combine this procedure with first-order ray tracing and dynamic ray tracing equations for P waves propagating in smooth, inhomogeneous, weakly anisotropic media. The first-order ray tracing and dynamic ray tracing equations are derived from the exact ones by replacing the exact P-wave eigenvalue of the Christoffel matrix by its first-order approximation. In the second part of the Master Thesis, we compute traveltimes using the fast marching method and we develop an approach to estimate the anisotropy parameters. The idea is to relate them analytically to traveltimes which is challenging in inhomogeneous media. Using perturbation theory, we develop traveltime approximations for transversely isotropic media with horizontal symmetry axis (HTI) as explicit functions of the
State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

Science.gov (United States)

Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

2016-01-15

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.
A systematic review of the efficacy and limitations of venous intervention in stasis ulceration.

Science.gov (United States)

Montminy, Myriam L; Jayaraj, Arjun; Raju, Seshadri

2018-05-01

Surgical techniques to address various components of chronic venous disease are rapidly evolving. Their efficacy and generally good results in treating superficial venous reflux (SVR) have been documented and compared in patients presenting with pain and swelling. A growing amount of literature is now available suggesting their efficacy in patients with venous leg ulcer (VLU). This review attempts to summarize the efficacy and limitations of commonly used venous interventions in the treatment of SVR and incompetent perforator veins (IPVs) in patients with VLU. A systematic review of the published literature was performed. Two different searches were conducted in MEDLINE, Embase, and EBSCOhost to identify studies that examined the efficacy of SVR ablation and IPV ablation on healing rate and recurrence rate of VLU. In the whole review, 1940 articles were screened. Of those, 45 were included in the SVR ablation review and 4 in the IPV ablation review. Data were too heterogeneous to perform an adequate meta-analysis. The quality of evidence assessed by the Grading of Recommendations Assessment, Development, and Evaluation for the two outcomes varied from very low to moderate. Ulcer healing rate and recurrence rate were between 70% and 100% and 0% and 49% in the SVR ablation review and between 59% and 93% and 4% and 33% in the IPV ablation review, respectively. To explain those variable results, limitations such as inadequate diagnostic techniques, saphenous size, concomitant calf pump dysfunction, and associated deep venous reflux are discussed. Currently available minimally invasive techniques correct most venous pathologic processes in chronic venous disease with a good sustainable healing rate. There are still specific diagnostic and efficacy limitations that mandate proper match of individual patients with the planned approach. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.
Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

Directory of Open Access Journals (Sweden)

Shokri Saeid

2015-01-01

Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.
Optimized support vector regression for drilling rate of penetration estimation

Science.gov (United States)

Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa

2015-12-01

In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Support vector regression to predict porosity and permeability: Effect of sample size

Science.gov (United States)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function

SNOW DEPTH ESTIMATION USING TIME SERIES PASSIVE MICROWAVE IMAGERY VIA GENETICALLY SUPPORT VECTOR REGRESSION (CASE STUDY URMIA LAKE BASIN

Directory of Open Access Journals (Sweden)

N. Zahir

2015-12-01

Full Text Available Lake Urmia is one of the most important ecosystems of the country which is on the verge of elimination. Many factors contribute to this crisis among them is the precipitation, paly important roll. Precipitation has many forms one of them is in the form of snow. The snow on Sahand Mountain is one of the main and important sources of the Lake Urmia’s water. Snow Depth (SD is vital parameters for estimating water balance for future year. In this regards, this study is focused on SD parameter using Special Sensor Microwave/Imager (SSM/I instruments on board the Defence Meteorological Satellite Program (DMSP F16. The usual statistical methods for retrieving SD include linear and non-linear ones. These methods used least square procedure to estimate SD model. Recently, kernel base methods widely used for modelling statistical problem. From these methods, the support vector regression (SVR is achieved the high performance for modelling the statistical problem. Examination of the obtained data shows the existence of outlier in them. For omitting these outliers, wavelet denoising method is applied. After the omission of the outliers it is needed to select the optimum bands and parameters for SVR. To overcome these issues, feature selection methods have shown a direct effect on improving the regression performance. We used genetic algorithm (GA for selecting suitable features of the SSMI bands in order to estimate SD model. The results for the training and testing data in Sahand mountain is [R²_TEST=0.9049 and RMSE= 6.9654] that show the high SVR performance.
Temperature-based estimation of global solar radiation using soft computing methodologies

Science.gov (United States)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Danesh, Amir Seyed; Abdullah, Mohd Shahidan; Zamani, Mazdak

2016-07-01

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures ( T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max- T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.
Noise reduction by support vector regression with a Ricker wavelet kernel

International Nuclear Information System (INIS)

Deng, Xiaoying; Yang, Dinghui; Xie, Jing

2009-01-01

We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR
Noise reduction by support vector regression with a Ricker wavelet kernel

Science.gov (United States)

Deng, Xiaoying; Yang, Dinghui; Xie, Jing

2009-06-01

We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR.
A hepatitis C virus infection model with time-varying drug effectiveness: solution and analysis.

Directory of Open Access Journals (Sweden)

Jessica M Conway

2014-08-01

Full Text Available Simple models of therapy for viral diseases such as hepatitis C virus (HCV or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.
Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

Directory of Open Access Journals (Sweden)

Shahrbanoo Goli

2016-01-01

Full Text Available The Support Vector Regression (SVR model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model.
LOW-MASS GALAXY FORMATION IN COSMOLOGICAL ADAPTIVE MESH REFINEMENT SIMULATIONS: THE EFFECTS OF VARYING THE SUB-GRID PHYSICS PARAMETERS

International Nuclear Information System (INIS)

ColIn, Pedro; Vazquez-Semadeni, Enrique; Avila-Reese, Vladimir; Valenzuela, Octavio; Ceverino, Daniel

2010-01-01

We present numerical simulations aimed at exploring the effects of varying the sub-grid physics parameters on the evolution and the properties of the galaxy formed in a low-mass dark matter halo (∼7 x 10 10 h -1 M sun at redshift z = 0). The simulations are run within a cosmological setting with a nominal resolution of 218 pc comoving and are stopped at z = 0.43. For simulations that cannot resolve individual molecular clouds, we propose the criterion that the threshold density for star formation, n SF , should be chosen such that the column density of the star-forming cells equals the threshold value for molecule formation, N ∼ 10 21 cm -2 , or ∼8 M sun pc -2 . In all of our simulations, an extended old/intermediate-age stellar halo and a more compact younger stellar disk are formed, and in most cases, the halo's specific angular momentum is slightly larger than that of the galaxy, and sensitive to the SF/feedback parameters. We found that a non-negligible fraction of the halo stars are formed in situ in a spheroidal distribution. Changes in the sub-grid physics parameters affect significantly and in a complex way the evolution and properties of the galaxy: (1) lower threshold densities n SF produce larger stellar effective radii R e , less peaked circular velocity curves V c (R), and greater amounts of low-density and hot gas in the disk mid-plane; (2) when stellar feedback is modeled by temporarily switching off radiative cooling in the star-forming regions, R e increases (by a factor of ∼2 in our particular model), the circular velocity curve becomes flatter, and a complex multi-phase gaseous disk structure develops; (3) a more efficient local conversion of gas mass to stars, measured by a stellar particle mass distribution biased toward larger values, increases the strength of the feedback energy injection-driving outflows and inducing burstier SF histories; (4) if feedback is too strong, gas loss by galactic outflows-which are easier to produce in low
The Effect of Stochastically Varying Creep Parameters on Residual Stresses in Ceramic Matrix Composites

Science.gov (United States)

Pineda, Evan J.; Mital, Subodh K.; Bednarcyk, Brett A.; Arnold, Steven M.

2015-01-01

Constituent properties, along with volume fraction, have a first order effect on the microscale fields within a composite material and influence the macroscopic response. Therefore, there is a need to assess the significance of stochastic variation in the constituent properties of composites at the higher scales. The effect of variability in the parameters controlling the time-dependent behavior, in a unidirectional SCS-6 SiC fiber-reinforced RBSN matrix composite lamina, on the residual stresses induced during processing is investigated numerically. The generalized method of cells micromechanics theory is utilized to model the ceramic matrix composite lamina using a repeating unit cell. The primary creep phases of the constituents are approximated using a Norton-Bailey, steady state, power law creep model. The effect of residual stresses on the proportional limit stress and strain to failure of the composite is demonstrated. Monte Carlo simulations were conducted using a normal distribution for the power law parameters and the resulting residual stress distributions were predicted.
Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Lifetime and latency analysis of IEEE 802.15.6 WBAN with interrupted sleep mechanism ... Estimation of stochastic environment force for master–slave robotic system ... squares support vector regression (LS-SVR) for modelling the growth time of the ... Mathematical modeling and analysis of WEDM machining parameters of ...
Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression

Directory of Open Access Journals (Sweden)

Naradasu Kumar Ravi

2013-01-01

Full Text Available Diesel engine designers are constantly on the look-out for performance enhancement through efficient control of operating parameters. In this paper, the concept of an intelligent engine control system is proposed that seeks to ensure optimized performance under varying operating conditions. The concept is based on arriving at the optimum engine operating parameters to ensure the desired output in terms of efficiency. In addition, a Support Vector Machines based prediction model has been developed to predict the engine performance under varying operating conditions. Experiments were carried out at varying loads, compression ratios and amounts of exhaust gas recirculation using a variable compression ratio diesel engine for data acquisition. It was observed that the SVM model was able to predict the engine performance accurately.
Variation in plasmonic (electronic) spectral parameters of Pr (III) and Nd (III) with varied concentration of moderators

Energy Technology Data Exchange (ETDEWEB)

Mishra, Shubha, E-mail: shubhamishra03@gmail.com [School of Studies in Physics, Vikram University, Ujjain (M. P.) (India); Limaye, S. N., E-mail: snl222@yahoo.co.in [Department of Chemistry, Dr. H.S. Gour University, A Central University, Sagar (M.P.) (India)

2015-07-31

It is said that the -4f shells behave as core and are least perturbed by changes around metal ion surrounding. However, there are evidences that-4f shells partially involved in direct moderator interaction. A systematic investigation on the plasmonic (electronic) spectral studies of some Rare Earths[RE(III).Mod] where, RE(III) = Pr(III),Nd(III) and Mod(moderator) = Y(III),La(III),Gd(III) and Lu(III), increased moderator concentration from 0.01 mol dm{sup −3} to 0.025 mol dm{sup −3} keeping the metal ion concentration at 0.01mol dm{sup −3} have been carried out. Variations in oscillator strengths (f), Judd-Ofelt parameters (T{sub λ}),inter-electronic repulsion Racah parameters (δE{sup k}),nephelauxetic ratio (β), radiative parameters (S{sub ED},A{sub T},β{sub R},T{sub R}). The values of oscillator strengths and Judd-Ofelt parameters have been discussed in the light of coordination number of RE(III) metal ions, denticity and basicity of the moderators. The [RE(III).Mod] bonding pattern has been studies in the light of the change in Racah parameters and nephelauxetic ratio.
Holographic dark energy with varying gravitational constant in Hořava-Lifshitz cosmology

Energy Technology Data Exchange (ETDEWEB)

Setare, M.R. [Department of Physics, University of Kurdistan, Pasdaran Ave., Sanandaj (Iran, Islamic Republic of); Jamil, Mubasher, E-mail: rezakord@ipm.ir, E-mail: mjamil@camp.nust.edu.pk [Center for Advanced Mathematics and Physics, National University of Sciences and Technology, Rawalpindi, 46000 (Pakistan)

2010-02-01

We investigate the holographic dark energy scenario with a varying gravitational constant in a flat background in the context of Hořava-Lifshitz gravity. We extract the exact differential equation determining the evolution of the dark energy density parameter, which includes G variation term. Also we discuss a cosmological implication of our work by evaluating the dark energy equation of state for low redshifts containing varying G corrections.
State and parameter estimation in biotechnical batch reactors

NARCIS (Netherlands)

Keesman, K.J.

2000-01-01

In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in
Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries

Directory of Open Access Journals (Sweden)

Rui Silva

2018-02-01

Full Text Available The performance of a support vector regression (SVR model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number of whole berries and the spectrum of each sample was collected during ripening using hyperspectral imaging in the range of 380–1028 nm. Touriga Franca (TF variety samples were collected for the 2012–2015 vintages, and Touriga Nacional (TN and Tinta Barroca (TB variety samples were collected for the 2013 vintage. These TF vintages were independently used to train, validate and test the SVR methodology; different combinations of TF vintages were used to train and test each model to assess the performance differences under wider and more variable datasets; the varieties that were not employed in the model training and validation (TB and TN were used to test the generalization ability of the SVR approach. Each case was tested using an external independent set (with data not included in the model training or validation steps. The best R2 results obtained with varieties and vintages not employed in the model’s training step were 0.89, 0.81 and 0.90, with RMSE values of 35.6 mg·L−1, 0.25 and 3.19 °Brix, for anthocyanin concentration, pH index and sugar content, respectively. The present results indicate a good overall performance for all cases, improving the state-of-the-art results for external test sets, and suggesting that a robust model, with a generalization capacity over different varieties and harvest years may be obtainable without further training, which makes this a very competitive approach when compared to the models from other authors, since it makes the problem significantly simpler and more cost-effective.
Weakly Coupled Oscillators in a Slowly Varying World

OpenAIRE

Park, Youngmin; Ermentrout, Bard

2016-01-01

We extend the theory of weakly coupled oscillators to incorporate slowly varying inputs and parameters. We employ a combination of regular perturbation and an adiabatic approximation to derive equations for the phase-difference between a pair of oscillators. We apply this to the simple Hopf oscillator and then to a biophysical model. The latter represents the behavior of a neuron that is subject to slow modulation of a muscarinic current such as would occur during transient attention through ...
Effect of viral suppression on hepatic venous pressure gradient in hepatitis C with cirrhosis and portal hypertension.

Science.gov (United States)

Afdhal, N; Everson, G T; Calleja, J L; McCaughan, G W; Bosch, J; Brainard, D M; McHutchison, J G; De-Oertel, S; An, D; Charlton, M; Reddy, K R; Asselah, T; Gane, E; Curry, M P; Forns, X

2017-10-01

Portal hypertension is a predictor of liver-related clinical events and mortality in patients with hepatitis C and cirrhosis. The effect of interferon-free hepatitis C treatment on portal pressure is unknown. Fifty patients with Child-Pugh-Turcotte (CPT) A and B cirrhosis and portal hypertension (hepatic venous pressure gradient [HVPG] >6 mm Hg) were randomized to receive 48 weeks of open-label sofosbuvir plus ribavirin at Day 1 or after a 24-week observation period. The primary endpoint was sustained virologic response 12 weeks after therapy (SVR12) in patients who received ≥1 dose of treatment. Secondary endpoints included changes in HVPG, laboratory parameters, and MELD and CPT scores. A subset of patients was followed 48 weeks posttreatment to determine late changes in HVPG. SVR12 occurred in 72% of patients (33/46). In the 37 patients with paired HVPG measurements at baseline and the end of treatment, mean HVPG decreased by -1.0 (SD 3.97) mm Hg. Nine patients (24%) had ≥20% decreases in HVPG during treatment. Among 39 patients with pretreatment HVPG ≥12 mm Hg, 27 (69%) achieved SVR12. Four of the 33 (12%) patients with baseline HVPG ≥12 mm Hg had HVPG <12 mm Hg at the end of treatment. Of nine patients with pretreatment HVPG ≥12 mm Hg who achieved SVR12 and completed 48 weeks of follow-up, eight (89%) had a ≥20% reduction in HVPG, and three reduced their pressure to <12 mm Hg. Patients with chronic HCV and compensated or decompensated cirrhosis who achieve SVR can have clinically meaningful reductions in HVPG at long-term follow-up. (EudraCT 2012-002457-29). © 2017 John Wiley & Sons Ltd.
Fatty liver in hepatitis C patients post-sustained virological response with direct-acting antivirals

Science.gov (United States)

Noureddin, Mazen; Wong, Micaela M; Todo, Tsuyoshi; Lu, Shelly C; Sanyal, Arun J; Mena, Edward A

2018-01-01

AIM To determine steatosis and fibrosis prevalence in hepatitis C patients after a sustained virological response achieved with direct-acting antivirals. METHODS Transient elastography with controlled attenuation parameter (CAP) was used to assess hepatic steatosis post-sustained virological response (SVR); the CAP technology was not available in the United States at study initiation. Liver stiffness/fibrosis was measured before and 47 wk after treatment completion. Patients with genotype 3 and patients with cirrhosis were excluded. RESULTS One hundred and one patients were included in the study. Post-SVR there were decreases from baseline in alanine aminotransferase (ALT) (63.1 to 17.8 U/L), aspartate aminotransferase (51.8 to 21.5 U/L) and fibrosis score (7.4 to 6.1 kPa) (P steatosis on CAP; of these, 6.25% had advanced fibrosis. Patients with steatosis had higher body mass index (29.0 vs 26.1 kg/m2), glucose (107.8 vs 96.6 mg/dL), ALT (20.4 vs 15.3 mg/dL), CAP score (296.3 vs 212.4 dB/m) and fibrosis score (7.0 vs 5.3 kPa); P steatosis had change in fibrosis score post-SVR (7.7 kPa vs 7.0 kPa and 7.0 kPa vs 5.3 kPa); alternatively, (P steatosis continued to have clinically significant stiffness (≥ 7 kPa). CONCLUSION Fatty liver is very common in hepatitis C virus (HCV) patients post-SVR. These patients continue to have elevated mean fibrosis score (≥ 7 kPa) compared to those without fatty liver; some have advanced fibrosis. Long term follow up is needed to assess steatosis and fibrosis in HCV patients post-SVR. PMID:29568207
The parameters of free play of light drilling installations with varying configuration of the floating base

Energy Technology Data Exchange (ETDEWEB)

Putov, B.I.

1980-01-01

A reduction in the free play may be achieved through the rational selection of the basic dimensions of the body elements, the disposition of the bodies one relative to the other and by the use of various means for passive stabilization. In the design of MPBU not only the mean values of the free play parameters, which characterize a floating base from the point of view of the time people stay on it, must be available, but also the maximal possible values of the parameters with one and the same wave state, which determine the safety of the operation of the drilling equipment. Studies in installations of various standard off shore drilling rigs showed that the mean values of the free play parameters for all off shore drilling rigs with wave heights from 0.8 to 1.2 meters are half the maximal values.
Effects of time-varying β in SNLS3 on constraining interacting dark energy models

International Nuclear Information System (INIS)

Wang, Shuang; Wang, Yong-Zhen; Geng, Jia-Jia; Zhang, Xin

2014-01-01

It has been found that, for the Supernova Legacy Survey three-year (SNLS3) data, there is strong evidence for the redshift evolution of the color-luminosity parameter β. In this paper, adopting the w-cold-dark-matter (wCDM) model and considering its interacting extensions (with three kinds of interaction between dark sectors), we explore the evolution of β and its effects on parameter estimation. In addition to the SNLS3 data, we also use the latest Planck distance priors data, the galaxy clustering data extracted from sloan digital sky survey data release 7 and baryon oscillation spectroscopic survey, as well as the direct measurement of Hubble constant H 0 from the Hubble Space Telescope observation. We find that, for all the interacting dark energy (IDE) models, adding a parameter of β can reduce χ 2 by ∝34, indicating that a constant β is ruled out at 5.8σ confidence level. Furthermore, it is found that varying β can significantly change the fitting results of various cosmological parameters: for all the dark energy models considered in this paper, varying β yields a larger fractional CDM densities Ω c0 and a larger equation of state w; on the other side, varying β yields a smaller reduced Hubble constant h for the wCDM model, but it has no impact on h for the three IDE models. This implies that there is a degeneracy between h and coupling parameter γ. Our work shows that the evolution of β is insensitive to the interaction between dark sectors, and then highlights the importance of considering β's evolution in the cosmology fits. (orig.)
Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

Science.gov (United States)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

KAUST Repository

Wang, Bing; Zhang, Jun; Chen, Peng; Ji, Zhiwei; Deng, Shuping; Li, Chi

2013-01-01

Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.
Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

KAUST Repository

Wang, Bing

2013-05-09

Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.
Orthoptic parameters and asthenopic symptoms analysis after 3D viewing at varying distances

Directory of Open Access Journals (Sweden)

Oleeviya Joseph

2018-05-01

Full Text Available AIM: To analyse visual modifications such as amplitude of accommodation, near point of convergence(NPCreopsis and near phoria associated with asthenopic symptoms after 3D viewing at varying distances.METHODS: A prospective study. Thirty young adults were randomly selected. Each individual was exposed to 3D viewing thrice in a day for a fixed distance and the distance was varied on three consecutive days. Same video of equal duration and different screen sizes were used for every distance. Cyclic 3D mode of K-multimedia(KMplayer was used for projecting the 3D video. Different variables like stereopsis, amplitude of accommodation, near point of accommodation, near phoria and asthenopic symptoms were recorded immediately after 3D video viewing. Stereopsis was measured with “Toegepast Natuurwetenschappelijk Onderzoek” or “Netherlands Organisation for Applied Scientific Research”(TNO test, amplitude of accommodation and NPC were measured using RAF ruler, near phoria was measured using prism bar and a closed ended sample questionnaire was used to know the occurrence of asthenopic symptoms. Statistical analyses were performed using descriptive statistics, paired t-test etc. Qualitative data was analyzed using Chi-square test.RESULTS: For every distance of 40 cm, 3 m and 6 m, amplitude of accommodation was significantly reduced by 0.66 D, 1.12 D and 1.44 D. NPC got significantly receded by 0.63 cm, 0.93 cm and 1.23 cm, and the near phoria was significantly increased by 0.87, and 2.2 prism dioptres(PDbase-in respectively. It was found that most of the subjects got pain around the eyes, headache and irritation for each viewing distance. This study also revealed that 3D video viewing in theaters may increase the symptoms of headache, watering and irritation. Symptoms like headache, watering, fatigue, irritation and nausea may increase considerably at home environment and symptoms such as headache and watering may cause significant discomfort by 3D
A new time-varying harmonic decomposition structure based on recursive hanning window

NARCIS (Netherlands)

Martins, C.H.; Silva, L.R.M.; Duque, C.A.; Cerqueira, A.S.; Teixeira, E.C.; Ribeiro, P.F.

2012-01-01

Analysis of power quality phenomena under time-varying conditions has become an important subject as the complexity of the grid increases. As a consequence, several methods have been developed/applied also to study power quality parameters during transient conditions such as time-frequency methods.
Boceprevir in genotype 1 chronic hepatitis C: First experiences in Serbia

Directory of Open Access Journals (Sweden)

Simonović-Babić Jasmina

2015-01-01

Full Text Available Introduction. The triple therapy which consists of one of the protease inhibitor plus pegylated interferon and ribavirin (P/R is the standard of care for the treatment of chronic hepatitis C virus (HCV genotype 1(G1 infection both in treatment-naпve and experienced patients. Objective. The aim of this study was to analyze the efficacy and tolerability of this regime in hospital practice in Serbia. Methods. From July 2012 to October 2012, 20 previously treated patients with advanced fibrosis and HCV G1 infection were included in the triple antiviral regimen in six referral centers in Serbia. All patients were treated with response guide therapy (RGT regime according to the boceprevir treatment protocol. During the 4-week lead-in period all patients received peginterferon plus ribavirin. After the lead-in period boceprevir was added in the dosage of 800 mg three times a day orally. The subsequent treatment varied according to virologic response and fibrosis. During the therapy HCV RNA level was measured at week 4, 8, 12, 24 of the treatment for the assessment of virologic response profile. All patients who completed therapy were assessed at the end of the treatment and at the end of an additional 24-week treatment-free period for a sustained virologic response (SVR. Results. The total of 20 patients with advanced fibrosis was treated. Among patients with an undetectable HCV RNA level at week 8 the rate of SVR was 100%. No patient with decrease in the HCV RNA level <1 log 10 IU/ml at treatment week 4 achieved SVR. The overall rate of SVR was 55%. The safety profile of the treatment regimen was good. Anemia was reported in 25% of patients. There was no life-threatening treatment adverse event. Conclusion. Boceprevir in combination with P/R achieved fairly good SVR rates in patients that were “most difficult to treat” who failed on dual therapy and was effective among patients with cirrhosis.
Performance of Traffic Noise Barriers with Varying Cross-Section

Directory of Open Access Journals (Sweden)

Sanja Grubeša

2011-05-01

Full Text Available The efficiency of noise barriers largely depends on their geometry. In this paper, the performance of noise barriers was simulated using the numerical Boundary Element Method (BEM. Traffic noise was particularly considered with its standardized noise spectrum adapted to human hearing. The cross-section of the barriers was varied with the goal of finding the optimum shape in comparison to classical rectangular barriers. The barrier performance was calculated at different receiver points for a fixed barrier height and source position. The magnitude of the insertion loss parameter was used to evaluate the performance change, both in one-third octave bands and as the broadband mean insertion loss value. The proposed barriers of varying cross-section were also compared with a typical T-shape barrier of the same height.
Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

Science.gov (United States)

Filgueiras, Paulo R; Terra, Luciana A; Castro, Eustáquio V R; Oliveira, Lize M S L; Dias, Júlio C M; Poppi, Ronei J

2015-09-01

This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS. Copyright © 2015 Elsevier B.V. All rights reserved.
Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data

Directory of Open Access Journals (Sweden)

Wenchen Shan

2018-01-01

Full Text Available Temperature variation has been widely demonstrated to produce significant effect on modal frequencies that even exceed the effect of actual damage. In order to eliminate the temperature effect on modal frequency, an effective method is to construct quantitative models which accurately predict the modal frequency corresponding to temperature variation. In this paper, principal component analysis (PCA is conducted on the temperatures taken from all embedded thermocouples for extracting input parameters of regression models. Three regression-based numerical models using multiple linear regression (MLR, back-propagation neural network (BPNN, and support vector regression (SVR techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. A comparison with respect to the performance of various optimally configured regression models has been performed on measurement data. Results indicate that the SVR exhibits a better reproduction and prediction capability than BPNN and MLR models for predicting the modal frequencies with respect to nonuniformly distributed temperatures. It is succeeded that temperature effects on modal frequencies can be effectively eliminated based on the optimally formulated SVR model.
Resonance parameter analysis with SAMMY

International Nuclear Information System (INIS)

Larson, N.M.; Perey, F.G.

1988-01-01

The multilevel R-matrix computer code SAMMY has evolved over the past decade to become an important analysis tool for neutron data. SAMMY uses the Reich-Moore approximation to the multilevel R-matrix and includes an optional logarithmic parameterization of the external R-function. Doppler broadening is simulated either by numerical integration using the Gaussian approximation to the free gas model or by a more rigorous solution of the partial differential equation equivalent to the exact free gas model. Resolution broadening of cross sections and derivatives also has new options that more accurately represent the experimental situation. SAMMY treats constant normalization and some types of backgrounds directly and treats other normalizations and/or backgrounds with the introduction of user-generated partial derivatives. The code uses Bayes' method as an efficient alternative to least squares for fitting experimental data. SAMMY allows virtually any parameter to be varied and outputs values, uncertainties, and covariance matrix for all varied parameters. Versions of SAMMY exist for VAX, FPS, and IBM computers
Insulin Sensitivity Determines Effects of Insulin and Meal Ingestion on Systemic Vascular Resistance in Healthy Subjects.

Science.gov (United States)

Woerdeman, Jorn; Meijer, Rick I; Eringa, Etto C; Hoekstra, Trynke; Smulders, Yvo M; Serné, Erik H

2016-01-01

In addition to insulin's metabolic actions, insulin can dilate arterioles which increase blood flow to metabolically active tissues. This effect is blunted in insulin-resistant subjects. Insulin's effect on SVR, determined by resistance arterioles, has, however, rarely been examined directly. We determined the effects of both hyperinsulinemia and a mixed meal on SVR and its relationship with insulin sensitivity. Thirty-seven lean and obese women underwent a hyperinsulinemic-euglycemic clamp, and 24 obese volunteers underwent a mixed-meal test. SVR was assessed using CPP before and during hyperinsulinemia as well as before and 60 and 120 minutes after a meal. SVR decreased significantly during hyperinsulinemia (-13%; p Insulin decreased SVR more strongly in insulin-sensitive individuals (standardized β: -0.44; p = 0.01). In addition, SVR at 60 minutes after meal ingestion was inversely related to the Matsuda index (β: -0.39; p = 0.04) and the change in postprandial SVR was directly related to postprandial glycemia (β: 0.53; p insulin resistance. This suggests that resistance to insulin-induced vasodilatation contributes to regulation of vascular resistance. © 2015 John Wiley & Sons Ltd.
Linear and support vector regressions based on geometrical correlation of data

Directory of Open Access Journals (Sweden)

Kaijun Wang

2007-10-01

Full Text Available Linear regression (LR and support vector regression (SVR are widely used in data analysis. Geometrical correlation learning (GcLearn was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation. This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.
Fault trend prediction of device based on support vector regression

International Nuclear Information System (INIS)

Song Meicun; Cai Qi

2011-01-01

The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)
Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights

NARCIS (Netherlands)

L.F. Hoogerheide (Lennart); R.H. Kleijn (Richard); H.K. van Dijk (Herman); M.J.C.M. Verbeek (Marno)

2009-01-01

textabstractSeveral Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time
Lyapunov Functions to Caputo Fractional Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Ravi Agarwal

2018-05-01

Full Text Available One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability and often the direct Lyapunov method is used to study stability properties (usually these Lyapunov functions do not depend on the time variable. In connection with the Lyapunov fractional method we present a brief overview of the most popular fractional order derivatives of Lyapunov functions among Caputo fractional delay differential equations. These derivatives are applied to various types of neural networks with variable coefficients and time-varying delays. We show that quadratic Lyapunov functions and their Caputo fractional derivatives are not applicable in some cases when one studies stability properties. Some sufficient conditions for stability of equilibrium of nonlinear Caputo fractional neural networks with time dependent transmission delays, time varying self-regulating parameters of all units and time varying functions of the connection between two neurons in the network are obtained. The cases of time varying Lipschitz coefficients as well as nonLipschitz activation functions are studied. We illustrate our theory on particular nonlinear Caputo fractional neural networks.
Modeling information diffusion in time-varying community networks

Science.gov (United States)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.
Optimization of space-time material layout for 1D wave propagation with varying mass and stiffness parameters

DEFF Research Database (Denmark)

Jensen, Jakob Søndergaard

2010-01-01

Results are presented for optimal layout of materials in the spatial and temporal domains for a 1D structure subjected to transient wave propagation. A general optimization procedure is outlined including derivation of design sensitivities for the case when the mass density and stiffness vary...
On generalized scaling laws with continuously varying exponents

International Nuclear Information System (INIS)

Sittler, Lionel; Hinrichsen, Haye

2002-01-01

Many physical systems share the property of scale invariance. Most of them show ordinary power-law scaling, where quantities can be expressed as a leading power law times a scaling function which depends on scaling-invariant ratios of the parameters. However, some systems do not obey power-law scaling, instead there is numerical evidence for a logarithmic scaling form, in which the scaling function depends on ratios of the logarithms of the parameters. Based on previous ideas by Tang we propose that this type of logarithmic scaling can be explained by a concept of local scaling invariance with continuously varying exponents. The functional dependence of the exponents is constrained by a homomorphism which can be expressed as a set of partial differential equations. Solving these equations we obtain logarithmic scaling as a special case. The other solutions lead to scaling forms where logarithmic and power-law scaling are mixed
Varying Coefficient Panel Data Model in the Presence of Endogenous Selectivity and Fixed Effects

OpenAIRE

Malikov, Emir; Kumbhakar, Subal C.; Sun, Yiguo

2013-01-01

This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the selection equation obta...
Effect of material parameters on the compactibility of backfill materials

International Nuclear Information System (INIS)

Keto, P.; Kuula-Vaeisaenen, P.; Ruuskanen, J.

2006-05-01

The effect of different parameters on compactibility of mixture of bentonite and ballast as well as Friedland-clay was studied in laboratory with two different types of compaction tests. The material parameters varied were grain size distribution of the ballast material, grain shape, water ratio and bentonite content (15/30%). The other parameters varied were salinity of the mixing water, mixing process and compaction method and energy. Ballast materials with varying grain size distributions were produced from Olkiluoto mica-gneiss with different type of crushing processes. In addition, sand was chosen for ballast material due to its uniform grain size distribution and rounded grain shape. The maximum grain size of the ballast materials was between 5-10 mm. When comparing the compactibility of ballast materials, the highest dry densities were gained for ballast materials with graded grain size distribution. The compaction behaviour of the tested bentonite ballast mixtures is dominated by the bentonite content. The other parameters varied did not have significant effect on the compactibility of the mixtures with bentonite content of 30%. This can be explained with the amount of bentonite that is higher than what is needed to fill up the volume between the ballast grains. The results gained with the two different compaction tests are comparable. Both the bentonite/ballast mixtures and the Friedland clay behaved similarly when compacted with three different compaction pressures (180, 540 and 980 kPa). (orig.)
Scale effects on spatially varying relationships between urban landscape patterns and water quality.

Science.gov (United States)

Sun, Yanwei; Guo, Qinghai; Liu, Jian; Wang, Run

2014-08-01

Scientific interpretation of the relationships between urban landscape patterns and water quality is important for sustainable urban planning and watershed environmental protection. This study applied the ordinary least squares regression model and the geographically weighted regression model to examine the spatially varying relationships between 12 explanatory variables (including three topographical factors, four land use parameters, and five landscape metrics) and 15 water quality indicators in watersheds of Yundang Lake, Maluan Bay, and Xinglin Bay with varying levels of urbanization in Xiamen City, China. A local and global investigation was carried out at the watershed-level, with 50 and 200 m riparian buffer scales. This study found that topographical features and landscape metrics are the dominant factors of water quality, while land uses are too weak to be considered as a strong influential factor on water quality. Such statistical results may be related with the characteristics of land use compositions in our study area. Water quality variations in the 50 m buffer were dominated by topographical variables. The impact of landscape metrics on water quality gradually strengthen with expanding buffer zones. The strongest relationships are obtained in entire watersheds, rather than in 50 and 200 m buffer zones. Spatially varying relationships and effective buffer zones were verified in this study. Spatially varying relationships between explanatory variables and water quality parameters are more diversified and complex in less urbanized areas than in highly urbanized areas. This study hypothesizes that all these varying relationships may be attributed to the heterogeneity of landscape patterns in different urban regions. Adjustment of landscape patterns in an entire watershed should be the key measure to successfully improving urban lake water quality.

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

Energy Technology Data Exchange (ETDEWEB)

Yang, Sen; Li, Chengwei, E-mail: heikuanghit@163.com [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China)

2016-06-15

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiation of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.
Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay

International Nuclear Information System (INIS)

Feng Wei; Yang, Simon X.; Fu Wei; Wu Haixia

2009-01-01

This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria.
The cost of treatment failure: resource use and costs incurred by hepatitis C virus genotype 1-infected patients who do or do not achieve sustained virological response to therapy.

Science.gov (United States)

Backx, M; Lewszuk, A; White, J R; Cole, J; Sreedharan, A; van Sanden, S; Diels, J; Lawson, A; Neal, K R; Wiselka, M J; Ito, T; Irving, W L

2014-03-01

Chronic hepatitis C virus (HCV) infection places a considerable economic burden on health services. Cost-effectiveness analyses of antiviral treatment for patients with chronic HCV infection are dependent on assumptions about cost reductions following sustained virological response (SVR) to therapy. This study quantified the medium-term difference in health resource usage and costs depending on treatment outcome. Retrospective chart review of patients with HCV genotype 1 infection who had received at least 2 months pegylated interferon and ribavirin therapy, with known treatment outcome was conducted. Disease status was categorized as chronic hepatitis, cirrhosis or decompensated liver disease. Health resource use was documented for each patient in each disease state. Unit costs were from the NHS 'Payment by Results' database and the British National Formulary. One hundred and ninety three patients (108 SVR, 85 non-SVR) with mean follow-up of 3.5 (SVR) and 4.9 (non-SVR) years were enrolled. No SVR patient progressed to a more severe liver disease state. Annual transition rates for non-SVR patients were 7.4% (chronic hepatitis to cirrhosis) and 4.9% (cirrhosis to decompensated liver disease). By extrapolation of modelled data over a 5-year post-treatment period, failure of patients with chronic hepatitis to achieve SVR was associated with a 13-fold increase (roughly £2300) in costs, whilst for patients who were retreated, the increase was 56-fold, equating to more than £10 000. Achievement of an SVR has significant effects on health service usage and costs. This work provides real-life data for future cost-effectiveness analyses related to the treatment for chronic HCV infection. © 2013 John Wiley & Sons Ltd.
Response-only modal identification using random decrement algorithm with time-varying threshold level

International Nuclear Information System (INIS)

Lin, Chang Sheng; Tseng, Tse Chuan

2014-01-01

Modal Identification from response data only is studied for structural systems under nonstationary ambient vibration. The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm with time-varying threshold level. In the conventional random decrement algorithm, the threshold level for evaluating random dec signatures is defined as the standard deviation value of response data of the reference channel. The distortion of random dec signatures may be, however, induced by the error involved in noise from the original response data in practice. To improve the accuracy of identification, a modification of the sampling procedure in random decrement algorithm is proposed for modal-parameter identification from the nonstationary ambient response data. The time-varying threshold level is presented for the acquisition of available sample time history to perform averaging analysis, and defined as the temporal root-mean-square function of structural response, which can appropriately describe a wide variety of nonstationary behaviors in reality, such as the time-varying amplitude (variance) of a nonstationary process in a seismic record. Numerical simulations confirm the validity and robustness of the proposed modal-identification method from nonstationary ambient response data under noisy conditions.
Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure

DEFF Research Database (Denmark)

Amado, Christina; Teräsvirta, Timo

multiplier type misspecification tests. Finite-sample properties of these procedures and tests are examined by simulation. An empirical application to daily stock returns and another one to daily exchange rate returns illustrate the functioning and properties of our modelling strategy in practice......In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either ad- ditive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change...... in the conditional and unconditional variances where the transition between regimes over time is smooth. A modelling strategy for these new time-varying parameter GARCH models is developed. It relies on a sequence of Lagrange multiplier tests, and the adequacy of the estimated models is investigated by Lagrange...
Optimal Laser Phototherapy Parameters for Pain Relief.

Science.gov (United States)

Kate, Rohit J; Rubatt, Sarah; Enwemeka, Chukuka S; Huddleston, Wendy E

2018-03-27

Studies on laser phototherapy for pain relief have used parameters that vary widely and have reported varying outcomes. The purpose of this study was to determine the optimal parameter ranges of laser phototherapy for pain relief by analyzing data aggregated from existing primary literature. Original studies were gathered from available sources and were screened to meet the pre-established inclusion criteria. The included articles were then subjected to meta-analysis using Cohen's d statistic for determining treatment effect size. From these studies, ranges of the reported parameters that always resulted into large effect sizes were determined. These optimal ranges were evaluated for their accuracy using leave-one-article-out cross-validation procedure. A total of 96 articles met the inclusion criteria for meta-analysis and yielded 232 effect sizes. The average effect size was highly significant: d = +1.36 (confidence interval [95% CI] = 1.04-1.68). Among all the parameters, total energy was found to have the greatest effect on pain relief and had the most prominent optimal ranges of 120-162 and 15.36-20.16 J, which always resulted in large effect sizes. The cross-validation accuracy of the optimal ranges for total energy was 68.57% (95% CI = 53.19-83.97). Fewer and less-prominent optimal ranges were obtained for the energy density and duration parameters. None of the remaining parameters was found to be independently related to pain relief outcomes. The findings of meta-analysis indicate that laser phototherapy is highly effective for pain relief. Based on the analysis of parameters, total energy can be optimized to yield the largest effect on pain relief.
Modelling and Control of Ionic Electroactive Polymer Actuators under Varying Humidity Conditions

Directory of Open Access Journals (Sweden)

S. Sunjai Nakshatharan

2018-02-01

Full Text Available In this work, we address the problem of position control of ionic electroactive polymer soft actuators under varying relative humidity conditions. The impact of humidity on the actuation performance of ionic actuators is studied through frequency response and impedance spectroscopy analysis. Considering the uncertain performance of the actuator under varying humidity conditions, an adaptable model using the neural network method is developed. The model uses relative humidity magnitude as one of the model parameters, making it robust to different environmental conditions. Utilizing the model, a closed-loop controller based on the model predictive controller is developed for position control of the actuator. The developed model and controller are experimentally verified and found to be capable of predicting and controlling the actuators with excellent tracking accuracy under relative humidity conditions varying in the range of 10–90%.
An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

Directory of Open Access Journals (Sweden)

Thomas R O'Brien

Full Text Available Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC.Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.
Change in lattice parameter of tantalum due to dissolved hydrogen

Directory of Open Access Journals (Sweden)

Gyanendra P. Tiwari

2012-06-01

Full Text Available The volume expansion of tantalum due to the dissolved hydrogen has been determined using Bragg equation. The hydrogen was dissolved in the pure tantalum metal at constant temperature (360 °C and constant pressure (132 mbar by varying the duration of hydrogen charging. The amount of dissolved hydrogen was within the solid solubility limit. The samples with different hydrogen concentration were analyzed by X-ray diffraction technique. Slight peak shifts as well as peak broadening were observed. The relative changes of lattice parameters plotted against the hydrogen concentration revealed that the lattice parameters varied linearly with the hydrogen concentration.
IRI profile parameters at equatorial latitudes

International Nuclear Information System (INIS)

Reinisch, B.W.; Huang Xueqin; Conway, J.

2002-01-01

The IRI bottom-side electron density profile is specified as a function of three parameters B0, B1, and D1 describing the F2 layer thickness and shape, and the shape of the F1 layer, respectively. Together with the URSI or CCIR coefficients for the F2 layer peak density and height, they completely specify the profiles as function of time, season and solar activity. In support of the international effort of determining the best set of parameters we have analyzed the diurnal variations of B0, B1, and D1 for Jicamarca for high solar activity during 1999 and 2000 for different seasons and magnetic activity. The B0 values vary from a minimum of ∼95 km at 0300 LT to ∼250 km at local noon (1700 UT). The diurnal variation is similar to the IRI2000 prediction. B1 varies from ∼1.9 at daytime to ∼2.2 at night. The value of D1 is ∼0.5. The parameters show little Kp dependence. Standard deviations are shown. We calculated the ionospheric total electron contents for March and April 1998 from the ionogram profiles at Jicamarca and compared them with IRI predictions using the IRI 2000 parameters. While there is fair agreement, a significant time shift of 1 to 2 hours occurs in the transition from night to daytime values. (author)
The computation of dynamic fractional difference parameter for S&P500 index

Science.gov (United States)

Pei, Tan Pei; Cheong, Chin Wen; Galagedera, Don U. A.

2015-10-01

This study evaluates the time-varying long memory behaviors of the S&P500 volatility index using dynamic fractional difference parameters. Time-varying fractional difference parameter shows the dynamic of long memory in volatility series for the pre and post subprime mortgage crisis triggered by U.S. The results find an increasing trend in the S&P500 long memory volatility for the pre-crisis period. However, the onset of Lehman Brothers event reduces the predictability of volatility series following by a slight fluctuation of the factional differencing parameters. After that, the U.S. financial market becomes more informationally efficient and follows a non-stationary random process.
Improving the security of optoelectronic delayed feedback system by parameter modulation and system coupling

Science.gov (United States)

Liu, Lingfeng; Miao, Suoxia; Cheng, Mengfan; Gao, Xiaojing

2016-02-01

A coupled system with varying parameters is proposed to improve the security of optoelectronic delayed feedback system. This system is coupled by two parameter-varied optoelectronic delayed feedback systems with chaotic modulation. Dynamics performance results show that this system has a higher complexity compared to the original one. Furthermore, this system can conceal the time delay effectively against the autocorrelation function and delayed mutual information method and can increase the dimension space of secure parameters to resist brute-force attack by introducing the digital chaotic systems.
HCV treatment rates and sustained viral response among people who inject drugs in seven UK sites: real world results and modelling of treatment impact.

Science.gov (United States)

Martin, N K; Foster, G R; Vilar, J; Ryder, S; Cramp, M E; Gordon, F; Dillon, J F; Craine, N; Busse, H; Clements, A; Hutchinson, S J; Ustianowski, A; Ramsay, M; Goldberg, D J; Irving, W; Hope, V; De Angelis, D; Lyons, M; Vickerman, P; Hickman, M

2015-04-01

Hepatitis C virus (HCV) antiviral treatment for people who inject drugs (PWID) could prevent onwards transmission and reduce chronic prevalence. We assessed current PWID treatment rates in seven UK settings and projected the potential impact of current and scaled-up treatment on HCV chronic prevalence. Data on number of PWID treated and sustained viral response rates (SVR) were collected from seven UK settings: Bristol (37-48% HCV chronic prevalence among PWID), East London (37-48%), Manchester (48-56%), Nottingham (37-44%), Plymouth (30-37%), Dundee (20-27%) and North Wales (27-33%). A model of HCV transmission among PWID projected the 10-year impact of (i) current treatment rates and SVR (ii) scale-up with interferon-free direct acting antivirals (IFN-free DAAs) with 90% SVR. Treatment rates varied from <5 to over 25 per 1000 PWID. Pooled intention-to-treat SVR for PWID were 45% genotypes 1/4 [95%CI 33-57%] and 61% genotypes 2/3 [95%CI 47-76%]. Projections of chronic HCV prevalence among PWID after 10 years of current levels of treatment overlapped substantially with current HCV prevalence estimates. Scaling-up treatment to 26/1000 PWID annually (achieved already in two sites) with IFN-free DAAs could achieve an observable absolute reduction in HCV chronic prevalence of at least 15% among PWID in all sites and greater than a halving in chronic HCV in Plymouth, Dundee and North Wales within a decade. Current treatment rates among PWID are unlikely to achieve observable reductions in HCV chronic prevalence over the next 10 years. Achievable scale-up, however, could lead to substantial reductions in HCV chronic prevalence. © 2014 The Authors Journal of Viral Hepatitis Published by John Wiley & Sons Ltd.
Impact of surgical ventricular reconstruction on sphericity index in patients with ischaemic cardiomyopathy: follow-up from the STICH trial.

Science.gov (United States)

Choi, Jin-Oh; Daly, Richard C; Lin, Grace; Lahr, Brian D; Wiste, Heather J; Beaver, Thomas M; Iacovoni, Attilio; Malinowski, Marcin; Friedrich, Ivar; Rouleau, Jean L; Favaloro, Roberto R; Sopko, George; Lang, Irene M; White, Harvey D; Milano, Carmelo A; Jones, Robert H; Lee, Kerry L; Velazquez, Eric J; Oh, Jae K

2015-04-01

We sought to evaluate associations between baseline sphericity index (SI) and clinical outcome, and changes in SI after coronary artery bypass graft (CABG) surgery with or without surgical ventricular reconstruction (SVR) in ischaemic cardiomyopathy patients enrolled in the SVR study (Hypothesis 2) of the Surgical Treatment for Ischemic Heart Failure (STICH) trial. Among 1000 patients in the STICH SVR study, we evaluated 546 patients (255 randomized to CABG alone and 291 to CABG + SVR) whose baseline SI values were available. SI was not significantly different between treatment groups at baseline. After 4 months, SI had increased in the CABG + SVR group, but was unchanged in the CABG alone group (0.69 ± 0.10 to 0.77 ± 0.12 vs. 0.67 ± 0.07 to 0.66 ± 0.09, respectively; P < 0.001). SI did not significantly change from 4 months to 2 years in either group. Although LV end-systolic volume and EF improved significantly more in the CABG + SVR group compared with CABG alone, the severity of mitral regurgitation significantly improved only in the CABG alone group, and the estimated LV filling pressure (E/A ratio) increased only in the CABG + SVR group. Higher baseline SI was associated with worse survival after surgery (hazard ratio 1.21, 95% confidence interval 1.02 - 1.43; P = 0.026). Survival was not significantly different by treatment strategy. Although SVR was designed to improve LV geometry, SI worsened after SVR despite improved LVEF and smaller LV volume. Survival was significantly better in patients with lower SI regardless of treatment strategy. © 2015 The Authors. European Journal of Heart Failure © 2015 European Society of Cardiology.
Robust stabilisation of time-varying delay systems with probabilistic uncertainties

Science.gov (United States)

Jiang, Ning; Xiong, Junlin; Lam, James

2016-09-01

For robust stabilisation of time-varying delay systems, only sufficient conditions are available to date. A natural question is as follows: if the existing sufficient conditions are not satisfied, and hence no controllers can be found, what can one do to improve the stability performance of time-varying delay systems? This question is addressed in this paper when there is a probabilistic structure on the parameter uncertainty set. A randomised algorithm is proposed to design a state-feedback controller, which stabilises the system over the uncertainty domain in a probabilistic sense. The capability of the designed controller is quantified by the probability of stability of the resulting closed-loop system. The accuracy of the solution obtained from the randomised algorithm is also analysed. Finally, numerical examples are used to illustrate the effectiveness and advantages of the developed controller design approach.
Mechanisms of Hepatitis C Viral Resistance to Direct Acting Antivirals.

Science.gov (United States)

Ahmed, Asma; Felmlee, Daniel J

2015-12-18

There has been a remarkable transformation in the treatment of chronic hepatitis C in recent years with the development of direct acting antiviral agents targeting virus encoded proteins important for viral replication including NS3/4A, NS5A and NS5B. These agents have shown high sustained viral response (SVR) rates of more than 90% in phase 2 and phase 3 clinical trials; however, this is slightly lower in real-life cohorts. Hepatitis C virus resistant variants are seen in most patients who do not achieve SVR due to selection and outgrowth of resistant hepatitis C virus variants within a given host. These resistance associated mutations depend on the class of direct-acting antiviral drugs used and also vary between hepatitis C virus genotypes and subtypes. The understanding of these mutations has a clear clinical implication in terms of choice and combination of drugs used. In this review, we describe mechanism of action of currently available drugs and summarize clinically relevant resistance data.
Robust stability of uncertain Markovian jumping Cohen-Grossberg neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Sheng Li; Yang Huizhong

2009-01-01

This paper considers the robust stability of a class of uncertain Markovian jumping Cohen-Grossberg neural networks (UMJCGNNs) with mixed time-varying delays. The parameter uncertainties are norm-bounded and the mixed time-varying delays comprise discrete and distributed time delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. An example is given to show the effectiveness of the proposed results.
On Weighted Support Vector Regression

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2014-01-01

We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

International Nuclear Information System (INIS)

Lins, Isis Didier; Droguett, Enrique López; Moura, Márcio das Chagas; Zio, Enrico; Jacinto, Carlos Magno

2015-01-01

Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR are point estimates whereas in order to take better informed decisions, an uncertainty assessment should be also carried out. For this, we apply bootstrap to SVR so as to obtain confidence and prediction intervals, without having to make any assumption about probability distributions and with good performance even when only a small data set is available. The bootstrapped SVR is first verified on Monte Carlo experiments and then is applied to a real case study concerning the prediction of degradation of a component from the offshore oil industry. The results obtained indicate that the bootstrapped SVR is a promising tool for providing reliable point and interval estimates, which can inform maintenance-related decisions on degrading components. - Highlights: • Bootstrap (pairs/residuals) and SVR are used as an uncertainty analysis framework. • Numerical experiments are performed to assess accuracy and coverage properties. • More bootstrap replications does not significantly improve performance. • Degradation of equipment of offshore oil wells is estimated by bootstrapped SVR. • Estimates about the scale growth rate can support maintenance-related decisions
Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

Science.gov (United States)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Two-dimensional phononic crystals with time-varying properties: a multiple scattering analysis

International Nuclear Information System (INIS)

Wright, D W; Cobbold, R S C

2010-01-01

Multiple scattering theory is a versatile two- and three-dimensional method for characterizing the acoustic wave transmission through many scatterers. It provides analytical solutions to wave propagation in scattering structures, and its computational complexity grows logarithmically with the number of scatterers. In this paper we show how the 2D method can be adapted to include the effects of time-varying material parameters. Specifically, a new T-matrix is defined to include the effects of frequency modulation that occurs in time-varying phononic crystals. Solutions were verified against finite difference time domain (FDTD) simulations and showed excellent agreement. This new method enables fast characterization of time-varying phononic crystals without the need to resort to lengthy FDTD simulations. Also, the method of combining T-matrices to form the T-supermatrix remains unchanged provided that the new matrix definitions are used. The method is quite compatible with existing implementations of multiple scattering theory and could be readily extended to three-dimensional multiple scattering theory
Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

OpenAIRE

Wei Feng; Simon X. Yang; Haixia Wu

2014-01-01

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported ...
Neutron fluctuations in a medium randomly varying in time

International Nuclear Information System (INIS)

Lenard, Pal; Imre, Pazsit

2005-01-01

The master equation approach, which has traditionally been used for the calculation of neutron fluctuations in zero power systems with constant parameters, is extended to a case when the parameters of the system change randomly in time. We consider a forward type master equation for the probability distribution of the number of particles in a multiplying system whose properties jump randomly between two discrete states, both with and without an external source. The first two factorial moments are calculated, including the covariance. This model can be considered the unification of stochastic methods that were used either in a constant multiplying medium via the master equation technique, or in a fluctuating medium via the Langevin technique. In contrast to these methods, the one presented here can calculate the inherent noise in time-varying systems. The results obtained show a much richer characteristics of the zero power noise than that in constant systems. Even the concept of criticality has to be given a probabilistic interpretation. The asymptotic behaviour of the variance will be also qualitatively different from that in constant systems. The covariance of the neutron number in a subcritical system with a source, and the corresponding power spectrum, shows both the inherent and parametrically induced noise components. The results are relevant in medium power subcritical systems where the zero power noise is still significant, but they also have a bearing on all types of branching processes, such as evolution of biological systems, spreading of epidemics etc., which are set in a time-varying environment. (authors)
Neutron fluctuations in a medium randomly varying in time

Energy Technology Data Exchange (ETDEWEB)

Lenard, Pal [KFKI Atomic Energy Research Institute, Budapest (Hungary); Imre, Pazsit [Chalmers Univ. of Technology, Dept. of Nuclear Engineering, SE, Goteborg (Sweden)

2005-07-01

The master equation approach, which has traditionally been used for the calculation of neutron fluctuations in zero power systems with constant parameters, is extended to a case when the parameters of the system change randomly in time. We consider a forward type master equation for the probability distribution of the number of particles in a multiplying system whose properties jump randomly between two discrete states, both with and without an external source. The first two factorial moments are calculated, including the covariance. This model can be considered the unification of stochastic methods that were used either in a constant multiplying medium via the master equation technique, or in a fluctuating medium via the Langevin technique. In contrast to these methods, the one presented here can calculate the inherent noise in time-varying systems. The results obtained show a much richer characteristics of the zero power noise than that in constant systems. Even the concept of criticality has to be given a probabilistic interpretation. The asymptotic behaviour of the variance will be also qualitatively different from that in constant systems. The covariance of the neutron number in a subcritical system with a source, and the corresponding power spectrum, shows both the inherent and parametrically induced noise components. The results are relevant in medium power subcritical systems where the zero power noise is still significant, but they also have a bearing on all types of branching processes, such as evolution of biological systems, spreading of epidemics etc., which are set in a time-varying environment. (authors)
Determination of complex microcalorimeter parameters with impedance measurements

International Nuclear Information System (INIS)

Saab, T.; Bandler, S.R.; Chervenak, J.; Figueroa-Feliciano, E.; Finkbeiner, F.; Iyomoto, N.; Kelley, R.L.; Kilbourne, C.A.; Lindeman, M.A.; Porter, F.S.; Sadleir, J.

2006-01-01

The proper understanding and modeling of a microcalorimeter's response requires accurate knowledge of a handful of parameters, such as C, G, α. While a few of these parameters are directly determined from the IV characteristics, some others, notoriously the heat capacity (C) and α, appear in degenerate combinations in most measurable quantities. The consideration of a complex microcalorimeter leads to an added ambiguity in the determination of the parameters. In general, the dependence of the microcalorimeter's complex impedance on these various parameters varies with frequency. This dependence allows us to determine individual parameters by fitting the prediction of the microcalorimeter model to impedance data. In this paper we describe efforts at characterizing the Goddard X-ray microcalorimeters. With the parameters determined by this method, we compare the pulse shape and noise spectra predictions to data taken with the same devices
Importance of hydrological parameters in contaminant transport modeling in a terrestrial environment

International Nuclear Information System (INIS)

Tsuduki, Katsunori; Matsunaga, Takeshi

2007-01-01

A grid type multi-layered distributed parameter model for calculating discharge in a watershed was described. Model verification with our field observation resulted in different sets of hydrological parameter values, all of which reproduced the observed discharge. The effect of those varied hydrological parameters on contaminant transport calculation was examined and discussed by simulation of event water transfer. (author)
Ensemble Kalman Filter Inference of Spatially-varying Manning’s n coefficients in the Coastal Ocean

KAUST Repository

Siripatana, Adil

2018-05-16

Ensemble Kalman (EnKF) filtering is an established framework for large scale state estimation problems. EnKFs can also be used for state-parameter estimation, using the so-called “Joint-EnKF” approach. The idea is simply to augment the state vector with the parameters to be estimated and assign invariant dynamics for the time evolution of the parameters. In this contribution, we investigate the efficiency of the Joint-EnKF for estimating spatially-varying Manning’s n coefficients used to define the bottom roughness in the Shallow Water Equations (SWEs) of a coastal ocean model.Observation System Simulation Experiments (OSSEs) are conducted using the ADvanced CIRCulation (ADCIRC) model, which solves a modified form of the Shallow Water Equations. A deterministic EnKF, the Singular Evolutive Interpolated Kalman (SEIK) filter, is used to estimate a vector of Manning’s n coefficients defined at the model nodal points by assimilating synthetic water elevation data. It is found that with reasonable ensemble size (O(10)), the filter’s estimate converges to the reference Manning’s field. To enhance performance, we have further reduced the dimension of the parameter search space through a Karhunen-Loéve (KL) expansion. We have also iterated on the filter update step to better account for the nonlinearity of the parameter estimation problem. We study the sensitivity of the system to the ensemble size, localization scale, dimension of retained KL modes, and number of iterations. The performance of the proposed framework in term of estimation accuracy suggests that a well-tuned Joint-EnKF provides a promising robust approach to infer spatially varying seabed roughness parameters in the context of coastal ocean modeling.
Core concept of fast power reactor with zero sodium void reactivity

International Nuclear Information System (INIS)

Matveev, V.I.; Chebeskov, A.N.; Krivitsky, I.Y.

1991-01-01

The paper presents a core concept of BN-800 - type fast power reactor with zero sodium void reactivity (SVR). Consideration is given to the layout-and some design features of such a core. Some considerations on the determination of the required SVR value as one of the fast reactor safety criteria in accidents with coolant boiling are presented. Some methodical considerations an the development of calculation models that give a correct description of the new core features are stated. The results of the integral SVR calculation studies are included. reactivity excursions under different scenarios of sodium boiling are estimated, some corrections into the calculated SVR value are discussed. (author)
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

International Nuclear Information System (INIS)

Wan Li; Zhou Qinghua

2007-01-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established
Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

Science.gov (United States)

Wan, Li; Zhou, Qinghua

2007-11-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established.
Evaluation of the Overall Costs for the Croatian Repository: Varying Site, Design and Financial Parameters

International Nuclear Information System (INIS)

Kucar-Dragicevic, S.; Subasic, D.; Lebegner, J.

2000-01-01

Preliminary preparations for the construction of a LILW repository in Croatia included a number of activities and projects related to the siting process, safety assessment, disposal technology and repository design, and public acceptance issues. Costs evaluations have always been a part of the developing project documentation. However, only the estimates of the facility construction and equipment acquisition costs had been included, while other costs associated with the project development and management have not been considered up to now. For the first time the infrastructure status at the potential sites has been evaluated, and the costs of the repository operations as well as the post-closure management has been estimated. Cost parameters have been considered from both technical and fiscal points of view, comparing their relative influence on the overall repository costs. Assessment of the total project costs in eight cases for the four preferential sites and two repository designs gave a clearer picture of the development and management costs differences for the considered options. Without considerations of the operational and post-operational repository management expenses, the total project costs appear to have been heavily underestimated. Also, while the construction costs for the tunnel and the surface type repositories are significantly different, this influence of the repository type on the total project costs becomes far less important when the later phases management expenses are added. Finally, the role of fiscal parameters may further diminish the site and technology impacts on the overall costs. (author)
Time-varying output performances of piezoelectric vibration energy harvesting under nonstationary random vibrations

Science.gov (United States)

Yoon, Heonjun; Kim, Miso; Park, Choon-Su; Youn, Byeng D.

2018-01-01

Piezoelectric vibration energy harvesting (PVEH) has received much attention as a potential solution that could ultimately realize self-powered wireless sensor networks. Since most ambient vibrations in nature are inherently random and nonstationary, the output performances of PVEH devices also randomly change with time. However, little attention has been paid to investigating the randomly time-varying electroelastic behaviors of PVEH systems both analytically and experimentally. The objective of this study is thus to make a step forward towards a deep understanding of the time-varying performances of PVEH devices under nonstationary random vibrations. Two typical cases of nonstationary random vibration signals are considered: (1) randomly-varying amplitude (amplitude modulation; AM) and (2) randomly-varying amplitude with randomly-varying instantaneous frequency (amplitude and frequency modulation; AM-FM). In both cases, this study pursues well-balanced correlations of analytical predictions and experimental observations to deduce the relationships between the time-varying output performances of the PVEH device and two primary input parameters, such as a central frequency and an external electrical resistance. We introduce three correlation metrics to quantitatively compare analytical prediction and experimental observation, including the normalized root mean square error, the correlation coefficient, and the weighted integrated factor. Analytical predictions are in an excellent agreement with experimental observations both mechanically and electrically. This study provides insightful guidelines for designing PVEH devices to reliably generate electric power under nonstationary random vibrations.
Parameter study on infilled steel frames with discretely connected precast concrete panels

NARCIS (Netherlands)

Teeuwen, P.A.; Kleinman, C.S.; Snijder, H.H.; Hofmeyer, H.; Chan, S.L.

2009-01-01

This paper presents a parameter study on infilled steel frames with discretely connected precast concrete infill panels having window openings. In this study, finite element simulations were carried out to study the infilled frame performance by varying several parameters. A recently developed
Frequency-scanning interferometry using a time-varying Kalman filter for dynamic tracking measurements.

Science.gov (United States)

Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen

2017-10-16

Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.
Time-varying exchange rate pass-through: experiences of some industrial countries

OpenAIRE

Toshitaka Sekine

2006-01-01

This paper estimates exchange rate pass-through of six major industrial countries using a time-varying parameter with stochastic volatility model. Exchange rate pass-through is divided into impacts of exchange rate fluctuations to import prices (first-stage pass-through) and those of import price movements to consumer prices (second-stage pass-through). The paper finds that both stages of pass-through have declined over time for all the sample countries. The decline in second-stage pass-throu...
Effects of varying laser trimming geometries on thin film\\ud resistors

OpenAIRE

Alafogianni, Maria; Birkett, Martin; Penlington, Roger

2017-01-01

Purpose - This paper studies the effects of varying laser trim patterns on several performance parameters of thin film resistors such as the temperature coefficient of resistance (TCR) and target resistance value.\\ud \\ud Design/methodology/approach - The benefits and limitations of basic trim patterns are taken into consideration and the plunge cut, double plunge cut and the curved L-cut were selected to be modelled and tested experimentally. A computer simulation of the laser trim patterns h...
Weight loss, leukopenia and thrombocytopenia associated with sustained virologic response to Hepatitis C treatment

Science.gov (United States)

Suwantarat, Nuntra; Tice, Alan D.; Khawcharoenporn, Thana; Chow, Dominic C.

2010-01-01

OBJECTIVE: To identify apparent adverse effects of treatment of chronic hepatitis C and their relationship to sustained virologic response (SVR). METHODS: A retrospective study was conducted of all Hepatitis C virus (HCV)-infected patients treated with pegylated interferon and ribavirin in an academic ambulatory infectious disease practice. Clinical and laboratory characteristics were compared between patients with SVR and without SVR. RESULTS: Fifty-four patients completed therapy with the overall SVR rate of 76%. SVR was associated with genotype non-1 (P=0.01), weight loss more than 5 kilograms (P=0.04), end of treatment leukopenia (P=0.02) and thrombocytopenia (P=0.05). In multivariate analysis, SVR was significant associated with HCV genotype non-1 (Adjusted Odd Ratio [AOR] 15.22; CI 1.55 to 149.72; P=0.02), weight loss more than 5 kilograms, (AOR 5.74; CI 1.24 to 26.32; P=0.04), and end of treatment white blood cell count level less than 3 X 103 cells/µl (AOR 9.09; CI 1.59 to 52.63; P=0.02). Thrombocytopenia was not significant after adjustment. Other factors including age, gender, ethnicity, injection drug use, viral load, anemia, alanine transaminase level, and liver histology did not reach statistical significance. CONCLUSION: Besides non-1 genotype, SVR was found to be independently associated with weight loss during therapy, and leukopenia at the end of HCV treatment. These correlations suggest continuation of therapy despite adverse effects, may be of benefit. PMID:20107528
The treatment of HCV in patients with haemoglobinopathy in Kurdistan Region, Iraq: a single centre experience.

Science.gov (United States)

Hussein, N R; Tunjel, I; Basharat, Z; Taha, A; Irving, W

2016-06-01

Various variables that might influence the rapid and sustained virological response to recombinant PEG-IFN-α-2a were explored in Iraqi HCV-infected patients with haemoglobinopathy. Forty-three patients were evaluated for the relationship between rapid virological response (RVR), IL-28B polymorphism, viral load, liver enzyme levels, blood group, ultrasound findings, or HCV genotype and the sustained virological response (SVR) achievement. The overall RVR was 55·81% while the overall SVR was 53·49%. SVR in patients that achieved RVR was 82·61% (P = 0·0004). A significant association was found between initial alanine transaminase levels and viral load with SVR achievement (P = 0·025) and (P = 0·004), respectively. Thirty-two (74%) out of 43 of our samples were host genotyped at the IL-28B locus as CC, a significant association was found between CC group and SVR achievement (P = 0·04). Of our samples, 23/43 (53%) were typed as HCV genotype 4, 10/43 (23%) as genotype 1, 9/43 (20·9%) as genotype 3 and 1/43 (2·3%) as genotype 2. A significant association was found between genotype 3 and SVR achievement (P = 0·006). Multivariate analysis showed that only RVR achievement independently associated with SVR in the Iraqi population (P = 0·00). These results can be used to classify the patients requiring the more expensive new direct-acting antiviral drugs.
Higher Ratio of Abdominal Subcutaneous to Visceral Adipose Tissue Related with Preservation of Islet β-Cell Function in Healthy Individuals

Directory of Open Access Journals (Sweden)

Juan Liu

2017-01-01

Full Text Available Objective. To investigate the relationship between abdominal adipose tissue distribution, β-cell function, and insulin sensitivity (IS in a Chinese population. Methods. One hundred and eighty-eight healthy subjects (healthy group, 239 with normal glucose, and 1~4 abnormal metabolic traits (metabolic dysfunction group, MD group and 125 with hyperglycemia (hyperglycemia group were studied. HOMA-IR, HOMA-B, Matsuda index, early- (I0–30/G0–30 and late-phase (I30–120/G30–120 insulin responses and the corresponding disposition indexes (DI were calculated. The area of abdominal subcutaneous adipose tissue (ASAT and visceral adipose tissue (VAT was measured and the ratio of ASAT to VAT (SVR was calculated. Results. SVR was correlated positively with Matsuda index in healthy, MD, and hyperglycemia groups, and inversely with HOMA-IR. SVR positively related with both early- and late-phase DI in the healthy group only. In the healthy group, the hyperbolas of I0–30/G0–30 and I30–120/G30–120 versus Matsuda index in the highest quarter of SVR were significantly right shifted compared to those in the lowest (both P<0.05. Conclusions. In healthy adults, higher SVR was a protective factor for β-cell function and IS, while in those with glucometabolic abnormality, higher SVR contributed to a relative better IS, indicating SVR is possible to be an early predicator of type 2 diabetes development.
[Clinical benefit of HCV core antigen assay in patients receiving interferon and ribavirin combination therapy].

Science.gov (United States)

Higashimoto, Makiko; Takahashi, Masahiko; Jokyu, Ritsuko; Saito, Hidetsugu

2006-02-01

A highly sensitive second generation HCV core antigen assay has recently been developed. We compared viral disappearance and kinetics data between commercially available core antigen assays, Lumipulse Ortho HCV Ag, and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor Test, Version 2 to estimate the predictive benefit of sustained viral response (SVR) and non-SVR in 59 patients treated with interferon and ribavirin combination therapy. We found a good correlation between HCV core Ag and HCV RNA level regardless of genotype. Although the sensitivity of the core antigen assay was lower than PCR, the dynamic range was broader than that of the PCR assay, so that we did not need to dilute the samples in 59 patients. We detected serial decline of core Ag levels in 24 hrs, 7 days and 14 days after interferon combination therapy. The decline of core antigen levels was significant in SVR patients compared to non-SVR as well as in genotype 2a, 2b patients compared to 1b. Core antigen-negative on day 1 could predict all 10 SVR patients (PPV = 100%), whereas RNA-negative could predict 22 SVR out of 25 on day 14 (PPV = 88.0%). None of the patients who had detectable serum core antigen on day 14 became SVR(NPV = 100%), although NPV was 91.2% on RNA negativity. An easy, simple, low cost new HCV core antigen detecting system seems to be useful for assessing and monitoring IFN treatment for HCV.

AN EFFECTIVE HYBRID SUPPORT VECTOR REGRESSION WITH CHAOS-EMBEDDED BIOGEOGRAPHY-BASED OPTIMIZATION STRATEGY FOR PREDICTION OF EARTHQUAKE-TRIGGERED SLOPE DEFORMATIONS

Directory of Open Access Journals (Sweden)

A. A. Heidari

2015-12-01

Full Text Available Earthquake can pose earth-shattering health hazards to the natural slops and land infrastructures. One of the chief consequences of the earthquakes can be land sliding, which is instigated by durable shaking. In this research, an efficient procedure is proposed to assist the prediction of earthquake-originated slope displacements (EIDS. New hybrid SVM-CBBO strategy is implemented to predict the EIDS. For this purpose, first, chaos paradigm is combined with initialization of BBO to enhance the diversification and intensification capacity of the conventional BBO optimizer. Then, chaotic BBO is developed as the searching scheme to investigate the best values of SVR parameters. In this paper, it will be confirmed that how the new computing approach is effective in prediction of EIDS. The outcomes affirm that the SVR-BBO strategy with chaos can be employed effectively as a predicting tool for evaluating the EIDS.
Models of quality-adjusted life years when health varies over time

DEFF Research Database (Denmark)

Hansen, Kristian Schultz; Østerdal, Lars Peter Raahave

2006-01-01

Qualityadjusted life year (QALY) models are widely used for economic evaluation in the health care sector. In the first part of the paper, we establish an overview of QALY models where health varies over time and provide a theoretical analysis of model identification and parameter estimation from...... time tradeoff (TTO) and standard gamble (SG) scores. We investigate deterministic and probabilistic models and consider five different families of discounting functions in all. The second part of the paper discusses four issues recurrently debated in the literature. This discussion includes questioning...... of these two can be used to disentangle risk aversion from discounting. We find that caution must be taken when drawing conclusions from models with chronic health states to situations where health varies over time. One notable difference is that in the former case, risk aversion may be indistinguishable from...
Progress on MEVVA source VARIS at GSI

Science.gov (United States)

Adonin, A.; Hollinger, R.

2018-05-01

For the last few years, the development of the VARIS (vacuum arc ion source) was concentrated on several aspects. One of them was the production of high current ion beams of heavy metals such as Au, Pb, and Bi. The requested ion charge state for these ion species is 4+. This is quite challenging to produce in vacuum arc driven sources for reasonable beam pulse length (>120 µs) due to the physical properties of these elements. However, the situation can be dramatically improved by using the composite materials or alloys with enhanced physical properties of the cathodes. Another aspect is an increase of the beam brilliance for intense U4+ beams by the optimization of the geometry of the extraction system. A new 7-hole triode extraction system allows an increase of the extraction voltage from 30 kV to 40 kV and also reduces the outer aperture of the extracted ion beam. Thus, a record beam brilliance for the U4+ beam in front of the RFQ (Radio-Frequency Quadrupole) has been achieved, exceeding the RFQ space charge limit for an ion current of 15 mA. Several new projectiles in the middle-heavy region have been successfully developed from VARIS to fulfill the requirements of the future FAIR (Facility for Antiproton and Ion Research) programs. An influence of an auxiliary gas on the production performance of certain ion charge states as well as on operation stability has been investigated. The optimization of the ion source parameters for a maximum production efficiency and highest particle current in front of the RFQ has been performed. The next important aspect of the development will be the increase of the operation repetition rate of VARIS for all elements especially for uranium to 2.7 Hz in order to provide the maximum availability of high current ion beams for future FAIR experiments.
Testing and estimating time-varying elasticities of Swiss gasoline demand

International Nuclear Information System (INIS)

Neto, David

2012-01-01

This paper is intended to test and estimate time-varying elasticities for gasoline demand in Switzerland. For this purpose, a smooth time-varying cointegrating parameters model is investigated in order to describe smooth mutations of the Swiss gasoline demand. The methodology, based on Chebyshev polynomials, is rigorously outlined. Our empirical finding states that the time-invariance assumption does not hold for long-run price and income elasticities. Furthermore they highlight that gasoline demand passed through some periods of sensitivity and non sensitivity with respect to the price. Our empirical statements are of great importance to assess the performance of a gasoline tax as an instrument for CO 2 reduction policy. Indeed, such an instrument can contribute to reduce emissions of greenhouse gases only if the demand is not fully inelastic with respect to the price. Our results suggest that such a carbon-tax would not be always suitable since the price elasticity is found not stable over time and not always significant.
Λ( t ) cosmology induced by a slowly varying Elko field

Energy Technology Data Exchange (ETDEWEB)

Pereira, S.H.; Pinho, A.S.S.; Silva, J.M. Hoff da [Universidade Estadual Paulista (Unesp), Faculdade de Engenharia, Guaratinguetá, Departamento de Física e Química Av. Dr. Ariberto Pereira da Cunha 333, 12516-410—Guaratinguetá, SP (Brazil); Jesus, J.F., E-mail: shpereira@feg.unesp.br, E-mail: alexandre.pinho510@gmail.com, E-mail: hoff@feg.unesp.br, E-mail: jfjesus@itapeva.unesp.br [Universidade Estadual Paulista (Unesp), Campus Experimental de Itapeva, R. Geraldo Alckmin, 519 Itapeva, SP (Brazil)

2017-01-01

In this work the exact Friedmann-Robertson-Walker equations for an Elko spinor field coupled to gravity in an Einstein-Cartan framework are presented. The torsion functions coupling the Elko field spin-connection to gravity can be exactly solved and the FRW equations for the system assume a relatively simple form. In the limit of a slowly varying Elko spinor field there is a relevant contribution to the field equations acting exactly as a time varying cosmological model Λ( t )=Λ{sub *}+3β H {sup 2}, where Λ{sub *} and β are constants. Observational data using distance luminosity from magnitudes of supernovae constraint the parameters Ω {sub m} and β, which leads to a lower limit to the Elko mass. Such model mimics, then, the effects of a dark energy fluid, here sourced by the Elko spinor field. The density perturbations in the linear regime were also studied in the pseudo-Newtonian formalism.
Haematological and Serum Biochemical Parameters of Broiler Chickens Fed Varying Dietary Levels of Fermented Castor Oil Seed Meal (Ricinus communis L. and Different Methionine Sources in South Western Nigeria

Directory of Open Access Journals (Sweden)

Ayorinde David Adeniran

2017-09-01

Full Text Available In this experiment, the effect of varying dietary levels of fermented castor oil seed meal (FCSM and different methionine sources (DL-methionine and herbal methionine on haematological and serum biochemical parameters of broilers. A total of 240 one-day-old Anak broiler chicks were used in the experiment lasted 56 days. The dietary experiment was laid out as a completely randomized design in a 4 × 2 factorial arrangement consisting of 4 dietary levels of FCSM (0, 50, 100 and 150 g/kg and 2 methionine sources (DL-methionine and herbal methionine. The birds were weighed and randomly distributed into 8 treatments with 3 replicates of 10 birds each. During the starter phase of the experiment, haemoglobin, red blood cell count, mean corpuscular haemoglobin concentration and eosinophil counts were higher (P
Stability parameters and their inter-relationships at the naviface

Digital Repository Service at National Institute of Oceanography (India)

Anto, A.F.; Hasse, L.; Murty, C.S.

Different forms of stability parameters used for the estimation of fluxes and studies on the structure of surface boundary layer of the marine atmosphere and their inter-relationships under the varying conditions of thermal stratifications...
Time-varying monetary-policy rules and financial stress: Does financial instability matter for monetary policy?

Czech Academy of Sciences Publication Activity Database

Baxa, Jaromír; Horváth, R.; Vašíček, B.

2013-01-01

Roč. 9, č. 1 (2013), s. 117-138 ISSN 1572-3089 Institutional support: RVO:67985556 Keywords : Financial stress * Time-varying parameter model * Endogenous regressors Subject RIV: AH - Economics Impact factor: 2.932, year: 2013 http://library.utia.cas.cz/separaty/2013/E/baxa-0395375.pdf
Effect of toxic substance on delayed competitive allelopathic phytoplankton system with varying parameters through stability and bifurcation analysis

International Nuclear Information System (INIS)

Pal, D.; Mahapatra, G.S.

2016-01-01

Highlights: • We study a delayed two species competitive system with imprecise biological parameters. • We consider impreciseness in the form of interval number. • We introduce parametric functional form of interval number to study the model. • We study the effect of toxicant and time delay under impreciseness. • We discuss the chaotic behavior of the model. - Abstract: We have studied the combined effect of toxicant and fluctuation of the biological parameters on the dynamical behaviors of a delayed two-species competitive system with imprecise biological parameters. Due to the global increase of harmful phytoplankton blooms, the study of dynamic interactions between two competing phytoplankton species in the presence of toxic substances is an active field of research now days. The ordinary mathematical formulation of models for two competing phytoplankton species, when one or both the species liberate toxic substances, is unable to capture the oscillatory and highly variable growth of phytoplankton populations. The deterministic model never predicts the sudden localized behavior of certain species. These obstacles of mathematical modeling can be overcomed if we include interval variability of biological parameters in our modeling approach. In this investigation, we construct imprecise models of allelopathic interactions between two competing phytoplankton species as a parametric differential equation model. We incorporate the effect of toxicant on the species in two different cases known as toxic inhibition and toxic stimulatory system. We have discussed the existence of various equilibrium points and stability of the system at these equilibrium points. In case of toxic stimulatory system, the delay model exhibits a stable limit cycle oscillation. Analytical findings are supported through exhaustive numerical simulations.
Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

Science.gov (United States)

Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

2016-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.
Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

Science.gov (United States)

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and
Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

DEFF Research Database (Denmark)

Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

1998-01-01

Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...
Acoustic cues for the recognition of self-voice and other-voice

Directory of Open Access Journals (Sweden)

Mingdi eXu

2013-10-01

Full Text Available Self-recognition, being indispensable for successful social communication, has become a major focus in current social neuroscience. The physical aspects of the self are most typically manifested in the face and voice. Compared with the wealth of studies on self-face recognition, self-voice recognition (SVR has not gained much attention. Converging evidence has suggested that the fundamental frequency (F0 and formant structures serve as the key acoustic cues for other-voice recognition (OVR. However, little is known about which, and how, acoustic cues are utilized for SVR as opposed to OVR. To address this question, we independently manipulated the F0 and formant information of recorded voices and investigated their contributions to SVR and OVR. Japanese participants were presented with recorded vocal stimuli and were asked to identify the speaker—either themselves or one of their peers. Six groups of 5 peers of the same sex participated in the study. Under conditions where the formant information was fully preserved and where only the frequencies lower than the third formant (F3 were retained, accuracies of SVR deteriorated significantly with the modulation of the F0, and the results were comparable for OVR. By contrast, under a condition where only the frequencies higher than F3 were retained, the accuracy of SVR was significantly higher than that of OVR throughout the range of F0 modulations, and the F0 scarcely affected the accuracies of SVR and OVR. Our results indicate that while both F0 and formant information are involved in SVR, as well as in OVR, the advantage of SVR is manifested only when major formant information for speech intelligibility is absent. These findings imply the robustness of self-voice representation, possibly by virtue of auditory familiarity and other factors such as its association with motor/articulatory representation.
Costs of telaprevir-based triple therapy for hepatitis C: $189,000 per sustained virological response.

Science.gov (United States)

Bichoupan, Kian; Martel-Laferriere, Valerie; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence; Moskowitz, Alan J; Dieterich, Douglas T; Branch, Andrea D

2014-10-01

In registration trials, triple therapy with telaprevir (TVR), pegylated interferon (Peg-IFN), and ribavirin (RBV) achieved sustained virological response (SVR) rates between 64% and 75%, but the clinical effectiveness and economic burdens of this treatment in real-world practice remain to be determined. Records of 147 patients who initiated TVR-based triple therapy at the Mount Sinai Medical Center (May-December 2011) were reviewed. Direct medical costs for pretreatment, on-treatment, and posttreatment care were calculated using data from Medicare reimbursement databases, RED Book, and the Healthcare Cost and Utilization Project database. Costs are presented in 2012 U.S. dollars. SVR (undetectable hepatitis C virus [HCV] RNA 24 weeks after the end of treatment) was determined on an intention-to-treat basis. Cost per SVR was calculated by dividing the median cost by the SVR rate. Median age of the 147 patients was 56 years (interquartile range [IQR] = 51-61), 68% were male, 19% were black, 11% had human immunodeficiency virus/HCV coinfection, 36% had advanced fibrosis/cirrhosis (FIB-4 scores ≥3.25), and 44% achieved an SVR. The total cost of care was $11.56 million. Median cost of care was $83,721 per patient (IQR = $66,652-$98,102). The median cost per SVR was $189,338 (IQR = $150,735-$221,860). Total costs were TVR (61%), IFN (24%), RBV (4%), adverse event management (8%), professional fees (2%), and laboratory tests (1%). TVR and Peg-IFN accounted for 85% of costs. Pharmaceutical prices and the low (44%) SVR rate, in this real-world study, were major contributors to the high cost per SVR. © 2014 by the American Association for the Study of Liver Diseases.
Hepatitis C virus cure does not impact kidney function decline in HIV co-infected patients.

Science.gov (United States)

Rossi, Carmine; Saeed, Sahar; Cox, Joseph; Vachon, Marie-Louise; Martel-Laferrière, Valérie; Walmsley, Sharon L; Cooper, Curtis; Gill, M John; Hull, Mark; Moodie, Erica E M; Klein, Marina B

2018-03-27

To examine the impact of sustained virologic response (SVR) and illicit (injection and noninjection) drug use on kidney function among hepatitis C virus (HCV) and HIV co-infected individuals. Longitudinal observational cohort study of HCV-HIV co-infected patients. Data from 1631 patients enrolled in the Canadian Co-Infection Cohort between 2003 and 2016 were analyzed. Patients who achieved SVR were matched 1 : 2 with chronically infected patients using time-dependent propensity scores. Linear regression with generalized estimating equations was used to model differences in estimated glomerular filtration rates (eGFR) between chronic HCV-infected patients and those achieving SVR. The relationship between illicit drug use and eGFR was explored in patients who achieved SVR. We identified 384 co-infected patients who achieved SVR (53% treated with interferon-free antiviral regimens) and 768 propensity-score matched patients with chronic HCV infection. Most patients were men (78%) and white (87%), with a median age of 51 years (interquartile range: 45-56). During 1767 person-years of follow-up, 4041 eGFR measurements were available for analysis. Annual rates of decline in eGFR were similar between patients with SVR [-1.32 (ml/min per 1.73 m)/year, 95% confidence interval (CI) -1.75 to -0.90] and chronic infection [-1.19 (ml/min per 1.73 m) per year, 95% CI -1.55 to -0.84]. Among SVR patients, recent injection cocaine use was associated with rapid eGFR decline [-2.16 (ml/min per 1.73 m)/year, 95% CI -4.17 to -0.16]. SVR did not reduce the rate of kidney function decline among HCV-HIV co-infected patients. Increased risk of chronic kidney disease in co-infection may not be related to persistent HCV replication but to ongoing injection cocaine use.
Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations

Science.gov (United States)

Rypdal, Martin; Sirnes, Espen; Løvsletten, Ola; Rypdal, Kristoffer

2013-08-01

Maximum likelihood estimation techniques for multifractal processes are applied to high-frequency data in order to quantify intermittency in the fluctuations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter λ characterising the degree of volatility clustering. We can therefore study the time evolution of volatility clustering and test the statistical significance of this variability. By analysing data from the Oslo Stock Exchange, and comparing the results with the investment grade spread, we find that the estimates of λ are lower at times of high market uncertainty.
Assessment of inotropic and vasodilating effects of milrinone lactate in patients with dilated cardiomyopathy and severe heart failure

Directory of Open Access Journals (Sweden)

Edson Antonio Bregagnollo

1999-02-01

Full Text Available OBJECTIVE: To assess the hemodynamic and vasodilating effects of milrinone lactate (ML in patients with dilated cardiomyopathy (DCM and New York Heart Association (NYHA class III and IV heart failure. METHODS: Twenty patients with DCM and NYHA class III and IV heart failure were studied. The hemodynamic and vasodilating effects of ML, administered intravenously, were evaluated. The following variables were compared before and during drug infusion: cardiac output (CO and cardiac index (CI; pulmonary capillary wedge pressure (PCWP; mean aortic pressure (MAP; mean pulmonary artery pressure (MPAP; mean right atrial pressure (MRAP; left ventricular systolic and end-diastolic pressures (LVSP and LVEDP, respectively; peak rate of left ventricular pressure rise (dP/dt; systemic vascular resistance (SVR; pulmonary vascular resistance (PVR; and heart rate (HR. RESULTS: All patients showed a significant improvement of the analysed parameters of cardiac performance with an increase of CO and CI; a significant improvement in myocardial contractility (dP/dt and reduction of the LVEDP; PCWP; PAP; MAP; MRAP; SVR; PVR. Were observed no significant increase in HR occurred. CONCLUSION: Milrinone lactate is an inotropic dilating drug that, when administered intravenously, has beneficial effects on cardiac performance and myocardial contractility. It also promotes reduction of SVR and PVR in patients with DCM and NYHA class III and IV of heart failure.
Estimation of the laser cutting operating cost by support vector regression methodology

Science.gov (United States)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.
Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

Directory of Open Access Journals (Sweden)

Fengxia Xu

2014-01-01

Full Text Available U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.
Novel criteria for global exponential periodicity and stability of recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Song Qiankun

2008-01-01

In this paper, the global exponential periodicity and stability of recurrent neural networks with time-varying delays are investigated by applying the idea of vector Lyapunov function, M-matrix theory and inequality technique. We assume neither the global Lipschitz conditions on these activation functions nor the differentiability on these time-varying delays, which were needed in other papers. Several novel criteria are found to ascertain the existence, uniqueness and global exponential stability of periodic solution for recurrent neural network with time-varying delays. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. Some previous results are improved and generalized, and an example is given to show the effectiveness of our method

Cogeneration: Key feasibility analysis parameters

International Nuclear Information System (INIS)

Coslovi, S.; Zulian, A.

1992-01-01

This paper first reviews the essential requirements, in terms of scope, objectives and methods, of technical/economic feasibility analyses applied to cogeneration systems proposed for industrial plants in Italy. Attention is given to the influence on overall feasibility of the following factors: electric power and fuel costs, equipment coefficients of performance, operating schedules, maintenance costs, Italian Government taxes and financial and legal incentives. Through an examination of several feasibility studies that were done on cogeneration proposals relative to different industrial sectors, a sensitivity analysis is performed on the effects of varying the weights of different cost benefit analysis parameters. With the use of statistical analyses, standard deviations are then determined for key analysis parameters, and guidelines are suggested for analysis simplifications
Program for parameter studies of steam generators

International Nuclear Information System (INIS)

Mathisen, R.P.

1982-11-01

R2-GEN is a computer code for stationary thermal parameter studies of steam generators. The geometry and data are valid for Ringhals-2 generators. Subroutines and relevant calculations are included. The program is based on a heterogeneous flow model and some applications on tubes with varying contamination are presented. (G.B.)
A statistical theory of cell killing by radiation of varying linear energy transfer

International Nuclear Information System (INIS)

Hawkins, R.B.

1994-01-01

A theory is presented that provides an explanation for the observed features of the survival of cultured cells after exposure to densely ionizing high-linear energy transfer (LET) radiation. It starts from a phenomenological postulate based on the linear-quadratic form of cell survival observed for low-LET radiation and uses principles of statistics and fluctuation theory to demonstrate that the effect of varying LET on cell survival can be attributed to random variation of dose to small volumes contained within the nucleus. A simple relation is presented for surviving fraction of cells after exposure to radiation of varying LET that depends on the α and β parameters for the same cells in the limit of low-LET radiation. This relation implies that the value of β is independent of LET. Agreement of the theory with selected observations of cell survival from the literature is demonstrated. A relation is presented that gives relative biological effectiveness (RBE) as a function of the α and β parameters for low-LET radiation. Measurements from microdosimetry are used to estimate the size of the subnuclear volume to which the fluctuation pertains. 11 refs., 4 figs., 2 tabs
Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings

International Nuclear Information System (INIS)

Dodoo, Ambrose; Tettey, Uniben Yao Ayikoe; Gustavsson, Leif

2017-01-01

In this study, we modelled the influence of different simulation assumptions on energy balances of two variants of a residential building, comprising the building in its existing state and with energy-efficient improvements. We explored how selected parameter combinations and variations affect the energy balances of the building configurations. The selected parameters encompass outdoor microclimate, building thermal envelope and household electrical equipment including technical installations. Our modelling takes into account hourly as well as seasonal profiles of different internal heat gains. The results suggest that the impact of parameter interactions on calculated space heating of buildings is somewhat small and relatively more noticeable for an energy-efficient building in contrast to a conventional building. We find that the influence of parameters combinations is more apparent as more individual parameters are varied. The simulations show that a building's calculated space heating demand is significantly influenced by how heat gains from electrical equipment are modelled. For the analyzed building versions, calculated final energy for space heating differs by 9–14 kWh/m"2 depending on the assumed energy efficiency level for electrical equipment. The influence of electrical equipment on calculated final space heating is proportionally more significant for an energy-efficient building compared to a conventional building. This study shows the influence of different simulation assumptions and parameter combinations when varied simultaneously. - Highlights: • Energy balances are modelled for conventional and efficient variants of a building. • Influence of assumptions and parameter combinations and variations are explored. • Parameter interactions influence is apparent as more single parameters are varied. • Calculated space heating demand is notably affected by how heat gains are modelled.
Pulsed Wave Doppler Ultrasound Is Useful to Assess Vasomotor Response in Patients with Multiple System Atrophy and Well Correlated with Tilt Table Study

Directory of Open Access Journals (Sweden)

Ke-Vin Chang

2012-01-01

Full Text Available The study aim was to assess sympathetic vasomotor response (SVR by using pulsed wave Doppler (PWD ultrasound in patients with multiple system atrophy (MSA and correlate with the tilt table study. We recruited 18 male patients and 10 healthy men as controls. The SVR of the radial artery was evaluated by PWD, using inspiratory cough as a provocative maneuver. The response to head-up tilt was studied by a tilt table with simultaneous heart rate and blood pressure recording. The hemodynamic variables were compared between groups, and were examined by correlation analysis. Regarding SVR, MSA patients exhibited a prolonged latency and less heart rate acceleration following inspiratory cough. Compared with the tilt table test, the elevation of heart rate upon SVR was positively correlated to the increase of heart rate after head-up tilt. The correlation analysis indicated that the magnitude of blood pressure drop from supine to upright was positively associated with the SVR latency but negatively correlated with the heart rate changes upon SVR. The present study demonstrated that blunted heart rate response might explain MSA's vulnerability to postural challenge. PWD may be used to predict cardiovascular response to orthostatic stress upon head-up tilt in MSA patients.
Linear Parameter Varying Control for Actuator Failure

National Research Council Canada - National Science Library

Shin, Jong-Yeob

2002-01-01

.... The estimates are provided on-line by a two-stage Kalman estimator. The inherent conservatism of the LPV design is reducing through the use of a scaling factor on the uncertainty block that represents the estimation errors of the effectiveness factors...
Gas metal arc welding of butt joint with varying gap width based on neural networks

DEFF Research Database (Denmark)

Christensen, Kim Hardam; Sørensen, Torben

2005-01-01

penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters, has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...
The impact of pegylated interferon and ribavirin combination treatment on lipid metabolism and insulin resistance in chronic hepatitis C patients.

Science.gov (United States)

Jung, Hee Jae; Kim, Young Seok; Kim, Sang Gyune; Lee, Yun Nah; Jeong, Soung Won; Jang, Jae Young; Lee, Sae Hwan; Kim, Hong Soo; Kim, Boo Sung

2014-03-01

Lipid profile and insulin resistance (IR) are associated with hepatitis C virus (HCV) and may predict the chronic hepatitis C (CHC) treatment response. The aim of this study was to determine the association between CHC treatment response and lipid profile and IR change during treatment. In total, 203 CHC patients were reviewed retrospectively between January 2005 and December 2011 at Soon Chun Hyang University Hospital. The lipid profile, homeostasis model for assessment (HOMA) of IR (HOMA-IR), and HOMA of β cells (HOMA-β) were evaluated before interferon plus ribavirin therapy (BTx), at the end of treatment (DTx), and 24 weeks after the end of treatment (ATx). A sustained virologic response (SVR) was achieved by 81% of all patients (49/60), 60% (n=36) of whom possessed genotype 1, with the remainder being non-genotype-1 (40%, n=24). Apart from age, which was significantly higher in the non-SVR group (SVR, 48.0 ± 11.2 years, mean ± SD; non-SVR, 56.6 ± 9.9 years; PC) had significantly changed at DTx and ATx compared to BTx. In addition, HOMA-IR and HOMA-β were significantly changed at DTx in the SVR group. Among those with a high baseline insulin resistance (HOMA-IR >2.5), HOMA-IR was significantly changed at DTx in the SVR group. LDL-C appears to be associated with HCV treatment in SVR patients. Furthermore, eradication of HCV may improve whole-body IR and insulin hypersecretion, as well as high baseline insulin resistance (HOMA-IR >2.5).
Hepatitis C virus therapy with peg-interferon and ribavirin in Myanmar: A resource-constrained country.

Science.gov (United States)

Hlaing, Naomi Khaing Than; Banerjee, Debolina; Mitrani, Robert; Arker, Soe Htet; Win, Kyaw San; Tun, Nyan Lin; Thant, Zaw; Win, Khin Maung; Reddy, K Rajender

2016-11-21

To investigate peg-interferon (peg-IFN) and ribavirin (RBV) therapy in Myanmar and to predict sustained virologic response (SVR). This single-center, open-label, study was conducted in Myanmar between 2009 and 2014. A total of 288 patients infected with HCV genotypes 1, 2, 3 and 6 were treated with peg-IFN alpha-2a (180 μg/wk) or alpha-2b (50 to 100 μg as a weight-based dose) and RBV as a weight-based dose (15 mg/kg/d). Treatment duration was 48 wk for genotypes 1 and 6, 24 wk for genotype 2, and 24 or 48 wk for genotype 3 based on rapid virologic response (RVR). Those co-infected with hepatitis B received 48 wk of therapy. Overall, SVR was achieved for 82% of patients and the therapy was well tolerated. All patients achieved SVR at equivalent rates regardless of HCV genotype ( P = 0.314). Low fibrosis scores ( P 96% positive predictive value for achieving SVR. Treatment duration did not significantly impact the likelihood of achieving SVR for patients infected with genotype 3 HCV ( P = 0.371). The most common adverse events were fatigue (71%) and poor appetite (60%). Among patients with genotype 3 HCV, more patients in the 48-wk treatment group required erythropoietin than in the 24-wk treatment group (61.1% vs 49.2%). SVR rates were high with peg-IFN and RBV therapy in Myanmar. Fibrosis scores, baseline albumin, HCV RNA levels and RVR independently predicted SVR.
Improvement of liver stiffness in patients with hepatitis C virus infection who received direct-acting antiviral therapy and achieved sustained virological response.

Science.gov (United States)

Tada, Toshifumi; Kumada, Takashi; Toyoda, Hidenori; Mizuno, Kazuyuki; Sone, Yasuhiro; Kataoka, Saki; Hashinokuchi, Shinichi

2017-12-01

There is insufficient research on whether direct-acting antiviral (DAA) therapy can improve liver fibrosis in patients with chronic hepatitis C virus (HCV). We evaluated sequential changes in liver stiffness using shear wave elastography in patients with HCV who received DAA therapy. A total of 210 patients with HCV who received daclatasvir and asunaprevir therapy and achieved sustained virological response (SVR) were analyzed. Liver stiffness, as evaluated by shear wave elastography, and laboratory data were assessed before treatment (baseline), at end of treatment (EOT), and at 24 weeks after EOT (SVR24). Alanine aminotransferase levels (ALT) decreased over time, and there were significant differences between baseline and EOT and between EOT and SVR24. Although platelet counts did not significantly differ between baseline and EOT, they increased significantly from EOT to SVR24. The median (interquartile range) liver stiffness values at baseline, EOT, and SVR24 were 10.2 (7.7-14.7), 8.8 (7.1-12.1), and 7.6 (6.3-10.3) kPa, respectively (P liver) and Fibrosis-4 index > 2.0 (n = 75), the liver stiffness values at baseline, EOT, and SVR24 were 9.6 (7.7-15.2), 9.2 (7.3-12.1), and 7.7 (6.3-10.1) kPa, respectively (P liver stiffness starts during the administration of DAAs in patients who achieve SVR, and this effect is particularly pronounced in patients with progressive liver fibrosis. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.
Observational constraints on holographic dark energy with varying gravitational constant

Energy Technology Data Exchange (ETDEWEB)

Lu, Jianbo; Xu, Lixin [Institute of Theoretical Physics, School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024 (China); Saridakis, Emmanuel N. [College of Mathematics and Physics, Chongqing University of Posts and Telecommunications, Chongqing, 400065 (China); Setare, M.R., E-mail: lvjianbo819@163.com, E-mail: msaridak@phys.uoa.gr, E-mail: rezakord@ipm.ir, E-mail: lxxu@dlut.edu.cn [Department of Science of Bijar, University of Kurdistan, Bijar (Iran, Islamic Republic of)

2010-03-01

We use observational data from Type Ia Supernovae (SN), Baryon Acoustic Oscillations (BAO), Cosmic Microwave Background (CMB) and observational Hubble data (OHD), and the Markov Chain Monte Carlo (MCMC) method, to constrain the cosmological scenario of holographic dark energy with varying gravitational constant. We consider both flat and non-flat background geometry, and we present the corresponding constraints and contour-plots of the model parameters. We conclude that the scenario is compatible with observations. In 1σ we find Ω{sub Λ0} = 0.72{sup +0.03}{sub −0.03}, Ω{sub k0} = −0.0013{sup +0.0130}{sub −0.0040}, c = 0.80{sup +0.19}{sub −0.14} and Δ{sub G}≡G'/G = −0.0025{sup +0.0080}{sub −0.0050}, while for the present value of the dark energy equation-of-state parameter we obtain w{sub 0} = −1.04{sup +0.15}{sub −0.20}.
Analysis on Passivity for Uncertain Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

O. M. Kwon

2014-01-01

Full Text Available The problem of passivity analysis for neural networks with time-varying delays and parameter uncertainties is considered. By the consideration of newly constructed Lyapunov-Krasovskii functionals, improved sufficient conditions to guarantee the passivity of the concerned networks are proposed with the framework of linear matrix inequalities (LMIs, which can be solved easily by various efficient convex optimization algorithms. The enhancement of the feasible region of the proposed criteria is shown via two numerical examples by the comparison of maximum allowable delay bounds.
The effect of selected parameters of the honing process on cylinder liner surface topography

International Nuclear Information System (INIS)

Pawlus, P; Dzierwa, A; Michalski, J; Reizer, R; Wieczorowski, M; Majchrowski, R

2014-01-01

Many truck cylinder liners made from gray cast iron were machined. Ceramic and diamond honing stones were used in the last stages of operation: coarse honing and plateau honing. The effect of honing parameters on the cylinder liner surface topography was studied. Selected surface topography parameters were response variables. It was found that parameters from the Sq group were sensitive to honing parameter change. When plateau honing time varied, the Smq parameter increased, while the other parameters, Spq and Svq, were stable. (papers)
A Comparative Study of Distribution System Parameter Estimation Methods

Energy Technology Data Exchange (ETDEWEB)

Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup

2016-07-17

In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Wei Feng

2014-01-01

Full Text Available The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.
Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Zhang, Yingchen

2016-11-14

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.
Indirect comparison of the antiviral efficacy of peginterferon alpha 2a plus ribavirin used with or without simeprevir in genotype 4 hepatitis C virus infection, where common comparator study arms are lacking: a special application of the matching adjusted indirect comparison methodology.

Science.gov (United States)

Van Sanden, Suzy; Pisini, Marta; Duchesne, Inge; Mehnert, Angelika; Belsey, Jonathan

2016-01-01

The need to assess relative efficacy in the absence of comparative clinical trials is a problem that is often encountered in economic modeling. The use of matching adjusted indirect comparison (MAIC) in this situation has been suggested. We present the results of a MAIC used to evaluate the incremental benefit offered by adding simeprevir (SMV) to standard therapy in the treatment of patients infected with genotype 4 hepatitis C virus (HCV). Individual patient data for a single arm study evaluating the use of SMV with peginterferon alfa 2a + ribavirin (PR) in genotype 4 HCV were available (RESTORE study). A systematic literature review was used to identify studies of PR alone used in the same patient group. By applying the inclusion criteria for each study in turn to the RESTORE dataset and then applying the published MAIC covariate matching algorithm, a series of pseudosamples from RESTORE were generated. After assessment of the matching outcomes, the best matched comparisons were used to derive estimates of efficacy for SMV + PR in patients equivalent to those participating in the PR trial. Five potential comparator studies were identified. After applying the matching process, two emerged as offering the greatest equivalence with the generated RESTORE pseudosamples and were used to estimate SMV + PR efficacy, expressed as the percentage of patients achieving sustained viral response (SVR). In one comparison, SVR in the SMV + PR group was 85% versus 63% for PR alone. In the second comparison, the corresponding SVRs were 77% and 44% respectively. After matching for varying baseline characteristics, both comparisons of RESTORE versus studies of PR alone yielded a benefit for SMV + PR vs PR alone in genotype 4 HCV-infected patients. The incremental gain in SVR associated with use of SMV ranged from 22% to 33%. In the absence of direct comparative studies, the MAIC gives a better perspective than simple comparison of absolute SVR from individual
Assessing the Long-Term Impact of Treating Hepatitis C Virus (HCV-Infected People Who Inject Drugs in the UK and the Relationship between Treatment Uptake and Efficacy on Future Infections.

Directory of Open Access Journals (Sweden)

Hayley Bennett

Full Text Available The prevalence of the hepatitis C virus (HCV remains high amongst people who inject drugs (PWID and accounts for the majority of newly acquired infections. This study aims to quantify the value of treatment amongst PWID with more efficacious treatments and at increased uptake rates, with respect to the avoidance of future infections and subsequent long-term complications of HCV.A dynamic HCV transmission and disease progression model was developed, incorporating acute and chronic infection and their long-term complications (decompensated cirrhosis, cancer, liver transplant and mortality, with the potential for HCV transmission to other PWID prior to successful treatment. The model was populated with prevalence and therapy data from a UK setting. Scenarios of current standard of care (SoC treatment efficacy and uptake were compared to anticipated sustained virologic response (SVR rates of 90-100% and increased uptake over varied horizons.SoC led to modest reductions in prevalence; >5% after 200 years. New treatments achieving 90% SVR could reduce prevalence below 5% within 60 years at current uptake rates or within 5 years if all patients are treated. Amongst 4,240 PWID, chronic HCV infections avoided as a result of increasing treatment uptake over the period 2015-2027 ranged from 20-580 and 34-912 with SoC and 90% SVR rates respectively. The reduction in downstream HCV infections due to increasing treatment uptake resulted in an approximate discounted gain of 300 life-years (from avoiding reduced life expectancy from HCV infection and a gain of 1,700 QALYs (from avoiding the disutility of HCV infection and related complications, with a projected £5.4 million cost saving.While improved SVR profiles led to reductions in modelled prevalence, increased treatment uptake was the key driver of future infections avoided. Increased treatment among PWID with new more efficacious therapies could significantly change the future dynamics, cost and health
Mechanisms of Hepatitis C Viral Resistance to Direct Acting Antivirals

Directory of Open Access Journals (Sweden)

Asma Ahmed

2015-12-01

Full Text Available There has been a remarkable transformation in the treatment of chronic hepatitis C in recent years with the development of direct acting antiviral agents targeting virus encoded proteins important for viral replication including NS3/4A, NS5A and NS5B. These agents have shown high sustained viral response (SVR rates of more than 90% in phase 2 and phase 3 clinical trials; however, this is slightly lower in real-life cohorts. Hepatitis C virus resistant variants are seen in most patients who do not achieve SVR due to selection and outgrowth of resistant hepatitis C virus variants within a given host. These resistance associated mutations depend on the class of direct-acting antiviral drugs used and also vary between hepatitis C virus genotypes and subtypes. The understanding of these mutations has a clear clinical implication in terms of choice and combination of drugs used. In this review, we describe mechanism of action of currently available drugs and summarize clinically relevant resistance data.
Analysis of cardiac energy metabolism in valve disease using 31P-MR-spectroscopy

International Nuclear Information System (INIS)

Beer, M.; Viehrig, M.; Seyfarth, T.; Sandstede, J.; Lipke, C.; Pabst, T.; Kenn, W.; Hahn, D.; Harre, K.; Horn, M.; Neubauer, S.; Landschuetz, W.; Kienlin, M. von

2000-01-01

Patients and methods: 10 healthy volunteers and 10 patients with aortic stenosis (pressure gradients >60 mmHg) were included. For assessment of energy metabolism, 31 P-MR spectra were obtained with a double oblique 3D-CSI technique (voxel size 25 cm 3 ). In 5 of 10 patients, follow-up examination was performed 3 months after surgical valve replacement (SVR). Left ventricular (LV) function was analyzed by cine MRI. Results: Before SVR the myocardial phosphocreatine to adenosinetriphosphate (PCr-ATP) ratio was significantly (p=0.0002) reduced to 0.80±0.25 in patients compared to 1.65±0.21 in volunteers. 3 months after SVR, LV mass had significantly (p=0.04) decreased from 238±33 g to 206±47 g. At the same time a significant (p=0.04) increase of the PCr-ATP ratio from 0.80±0.25 to 1.28±0.22 was observed. A slight, but not significant, reduction of the phosphodiester ATP ratio was observed before SVR, with a trend towards normalization after SVR. (orig.) [de

Evaluation of modulation transfer function of optical lens system by support vector regression methodologies - A comparative study

Science.gov (United States)

Petković, Dalibor; Shamshirband, Shahaboddin; Saboohi, Hadi; Ang, Tan Fong; Anuar, Nor Badrul; Rahman, Zulkanain Abdul; Pavlović, Nenad T.

2014-07-01

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict estimate MTF value of the actual optical system according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR_rbf approach in compare to SVR_poly soft computing methodology.
Image superresolution using support vector regression.

Science.gov (United States)

Ni, Karl S; Nguyen, Truong Q

2007-06-01

A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
Dependence of plasma characteristics on dc magnetron sputter parameters

International Nuclear Information System (INIS)

Wu, S.Z.

2005-01-01

Plasma discharge characteristics of a dc magnetron system were measured by a single Langmuir probe at the center axis of the dual-side process chamber. Plasma potential, floating potential, electron and ion densities, and electron temperature were extracted with varying dc power and gas pressure during sputter deposition of a metal target; strong correlations were shown between these plasma parameters and the sputter parameters. The electron density was controlled mostly by secondary electron generation in constant power mode, while plasma potential reflects the confinement space variation due to change of discharge voltage. When discharge pressure was varied, plasma density increases with the increased amount of free stock molecules, while electron temperature inversely decreased, due to energy-loss collision events. In low-pressure discharges, the electron energy distribution function measurements show more distinctive bi-Maxwellian distribution, with the fast electron temperature gradually decreases with increased gas pressure
Discrete simulations of spatio-temporal dynamics of small water bodies under varied stream flow discharges

Science.gov (United States)

Daya Sagar, B. S.

2005-01-01

Spatio-temporal patterns of small water bodies (SWBs) under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs) controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.
Discrete simulations of spatio-temporal dynamics of small water bodies under varied stream flow discharges

Directory of Open Access Journals (Sweden)

B. S. Daya Sagar

2005-01-01

Full Text Available Spatio-temporal patterns of small water bodies (SWBs under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.
A computationally efficient electricity price forecasting model for real time energy markets

International Nuclear Information System (INIS)

Feijoo, Felipe; Silva, Walter; Das, Tapas K.

2016-01-01

Highlights: • A fast hybrid forecast model for electricity prices. • Accurate forecast model that combines K-means and machine learning techniques. • Low computational effort by elimination of feature selection techniques. • New benchmark results by using market data for year 2012 and 2015. - Abstract: Increased significance of demand response and proliferation of distributed energy resources will continue to demand faster and more accurate models for forecasting locational marginal prices. This paper presents such a model (named K-SVR). While yielding prediction accuracy comparable with the best known models in the literature, K-SVR requires a significantly reduced computational time. The computational reduction is attained by eliminating the use of a feature selection process, which is commonly used by the existing models in the literature. K-SVR is a hybrid model that combines clustering algorithms, support vector machine, and support vector regression. K-SVR is tested using Pennsylvania–New Jersey–Maryland market data from the periods 2005–6, 2011–12, and 2014–15. Market data from 2006 has been used to measure performance of many of the existing models. Authors chose these models to compare performance and demonstrate strengths of K-SVR. Results obtained from K-SVR using the market data from 2012 and 2015 are new, and will serve as benchmark for future models.
The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis

Directory of Open Access Journals (Sweden)

He Yan

2016-01-01

Full Text Available The particle swarm optimization (PSO is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO.
The Influence of Slowly Varying Mass on Severity of Dynamics Nonlinearity of Bearing-Rotor Systems with Pedestal Looseness

Directory of Open Access Journals (Sweden)

Mian Jiang

2018-01-01

Full Text Available Nonlinearity measure is proposed to investigate the influence of slowly varying mass on severity of dynamics nonlinearity of bearing-rotor systems with pedestal looseness. A nonlinear mathematical model including the effect of slowly varying disk mass is developed for a bearing-rotor system with pedestal looseness. The varying of equivalent disk mass is described by a cosine function, and the amplitude coefficient is used as a control parameter. Then, nonlinearity measure is employed to quantify the severity of dynamics nonlinearity of bearing-rotor systems. With the increasing of looseness clearances, the curves that denote the trend of nonlinearity degree are plotted for each amplitude coefficient of mass varying. It can be concluded that larger amplitude coefficients of the disk mass varying will have more influence on the severity of dynamics nonlinearity and generation of chaotic behaviors in rotor systems with pedestal looseness.
Real-world cure rates for hepatitis C virus treatments that include simeprevir and/or sofosbuvir are comparable to clinical trial results.

Science.gov (United States)

Bichoupan, Kian; Tandon, Neeta; Crismale, James F; Hartman, Joshua; Del Bello, David; Patel, Neal; Chekuri, Sweta; Harty, Alyson; Ng, Michel; Sigel, Keith M; Bansal, Meena B; Grewal, Priya; Chang, Charissa Y; Leong, Jennifer; Im, Gene Y; Liu, Lawrence U; Odin, Joseph A; Bach, Nancy; Friedman, Scott L; Schiano, Thomas D; Perumalswami, Ponni V; Dieterich, Douglas T; Branch, Andrea D

2017-11-12

To assess the real-world effectiveness and cost of simeprevir (SMV), and/or sofosbuvir (SOF)-based therapy for chronic hepatitis C virus (HCV) infection. The real-world performance of patients treated with SMV/SOF ± ribavirin (RBV), SOF/RBV, and SOF/RBV with pegylated-interferon (PEG) were analyzed in a consecutive series of 508 patients with chronic HCV infection treated at a single academic medical center. Patients with genotypes 1 through 4 were included. Rates of sustained virological response - the absence of a detectable serum HCV RNA 12 wk after the end of treatment [sustained virological response (SVR) 12] - were calculated on an intention-to-treat basis. Costs were calculated from the payer's perspective using Medicare/Medicaid fees and Redbook Wholesale Acquisition Costs. Patient-related factors associated with SVR12 were identified using multivariable logistic regression. SVR12 rates were as follows: 86% (95%CI: 80%-91%) among 178 patients on SMV/SOF ± RBV; 62% (95%CI: 55%-68%) among 234 patients on SOF/RBV; and 78% (95%CI: 68%-86%) among 96 patients on SOF/PEG/RBV. Mean costs-per-SVR12 were $174442 (standard deviation: ± $18588) for SMV/SOF ± RBV; $223003 (± $77946) for SOF/RBV; and $126496 (± $31052) for SOF/PEG/RBV. Among patients on SMV/SOF ± RBV, SVR12 was less likely in patients previously treated with a protease inhibitor [odds ratio (OR): 0.20, 95%CI: 0.06-0.56]. Higher bilirubin (OR: 0.47, 95%CI: 0.30-0.69) reduced the likelihood of SVR12 among patients on SOF/RBV, while FIB-4 score ≥ 3.25 reduced the likelihood of SVR12 (OR: 0.18, 95%CI: 0.05-0.59) among those on SOF/PEG/RBV. SVR12 rates for SMV and/or SOF-based regimens in a diverse real-world population are comparable to those in clinical trials. Treatment failure accounts for 27% of costs.
Liver Stiffness Decreases Rapidly in Response to Successful Hepatitis C Treatment and Then Plateaus.

Directory of Open Access Journals (Sweden)

Sweta Chekuri

Full Text Available To investigate the impact of a sustained virological response (SVR to hepatitis C virus (HCV treatment on liver stiffness (LS.LS, measured by transient elastography (FibroScan, demographic and laboratory data of patients treated with interferon (IFN-containing or IFN-free regimens who had an SVR24 (undetectable HCV viral load 24 weeks after the end of treatment were analyzed using two-tailed paired t-tests, Mann-Whitney Wilcoxon Signed-rank tests and linear regression. Two time intervals were investigated: pre-treatment to SVR24 and SVR24 to the end of follow-up. LS scores ≥ 12.5 kPa indicated LS-defined cirrhosis. A p-value below 0.05 was considered statistically significant.The median age of the patients (n = 100 was 60 years [IQR (interquartile range 54-64; 72% were male; 60% were Caucasian; and 42% had cirrhosis pre-treatment according to the FibroScan measurement. The median LS score dropped from 10.40 kPa (IQR: 7.25-18.60 pre-treatment to 7.60 kPa (IQR: 5.60-12.38 at SVR24, p <0.01. Among the 42 patients with LS-defined cirrhosis pre-treatment, 25 (60% of patients still had LS scores ≥ 12.5 kPa at SVR24, indicating the persistence of cirrhosis. The median change in LS was similar in patients receiving IFN-containing and IFN-free regimens: -1.95 kPa (IQR: -5.75 --0.38 versus -2.40 kPa (IQR: -7.70 --0.23, p = 0.74. Among 56 patients with a post-SVR24 LS measurement, the LS score changed by an additional -0.90 kPa (IQR: -2.98-0.5 during a median follow-up time of 1.17 (IQR: 0.88-1.63 years, which was not a statistically significant decrease (p = 0.99.LS decreased from pre-treatment to SVR24, but did not decrease significantly during additional follow-up. Earlier treatment may be needed to reduce the burden of liver disease.
Varying coefficients model with measurement error.

Science.gov (United States)

Li, Liang; Greene, Tom

2008-06-01

We propose a semiparametric partially varying coefficient model to study the relationship between serum creatinine concentration and the glomerular filtration rate (GFR) among kidney donors and patients with chronic kidney disease. A regression model is used to relate serum creatinine to GFR and demographic factors in which coefficient of GFR is expressed as a function of age to allow its effect to be age dependent. GFR measurements obtained from the clearance of a radioactively labeled isotope are assumed to be a surrogate for the true GFR, with the relationship between measured and true GFR expressed using an additive error model. We use locally corrected score equations to estimate parameters and coefficient functions, and propose an expected generalized cross-validation (EGCV) method to select the kernel bandwidth. The performance of the proposed methods, which avoid distributional assumptions on the true GFR and residuals, is investigated by simulation. Accounting for measurement error using the proposed model reduced apparent inconsistencies in the relationship between serum creatinine and GFR among different clinical data sets derived from kidney donor and chronic kidney disease source populations.
Accurate step-hold tracking of smoothly varying periodic and aperiodic probability.

Science.gov (United States)

Ricci, Matthew; Gallistel, Randy

2017-07-01

Subjects observing many samples from a Bernoulli distribution are able to perceive an estimate of the generating parameter. A question of fundamental importance is how the current percept-what we think the probability now is-depends on the sequence of observed samples. Answers to this question are strongly constrained by the manner in which the current percept changes in response to changes in the hidden parameter. Subjects do not update their percept trial-by-trial when the hidden probability undergoes unpredictable and unsignaled step changes; instead, they update it only intermittently in a step-hold pattern. It could be that the step-hold pattern is not essential to the perception of probability and is only an artifact of step changes in the hidden parameter. However, we now report that the step-hold pattern obtains even when the parameter varies slowly and smoothly. It obtains even when the smooth variation is periodic (sinusoidal) and perceived as such. We elaborate on a previously published theory that accounts for: (i) the quantitative properties of the step-hold update pattern; (ii) subjects' quick and accurate reporting of changes; (iii) subjects' second thoughts about previously reported changes; (iv) subjects' detection of higher-order structure in patterns of change. We also call attention to the challenges these results pose for trial-by-trial updating theories.
A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.

Science.gov (United States)

Ren, Ye; Suganthan, Ponnuthurai Nagaratnam; Srikanth, Narasimalu

2016-08-01

Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity.
Liver-related morbidity and mortality in patients with chronic hepatitis C and cirrhosis with and without sustained virologic response

DEFF Research Database (Denmark)

Hallager, Sofie; Ladelund, Steen; Christensen, Peer Brehm

2017-01-01

Background: Chronic hepatitis C (CHC) causes liver cirrhosis in 5%-20% of patients, leading to increased morbidity and mortality. This study aimed to estimate liver-related morbidity and mortality among patients with CHC and cirrhosis in Denmark with and without antiviral treatment and sustained......, and 233 of 519 treated patients achieved SVR. Alcohol overuse and hepatitis C virus genotype 3 were associated with an increased incidence rate (IR) of HCC, whereas diabetes and alcohol overuse were associated with increased IRs of decompensation. Achieving SVR reduced all-cause mortality (adjusted...... elevated in patients with alcohol overuse after SVR. Conclusion: Alcohol overuse, hepatitis C genotype 3, and diabetes were associated with liver-related morbidity in patients with CHC and cirrhosis. SVR markedly reduced liver-related morbidity and mortality; however, special attention to patients...
Some new results for recurrent neural networks with varying-time coefficients and delays

International Nuclear Information System (INIS)

Jiang Haijun; Teng Zhidong

2005-01-01

In this Letter, we consider the recurrent neural networks with varying-time coefficients and delays. By constructing new Lyapunov functional, introducing ingeniously many real parameters and applying the technique of Young inequality, we establish a series of criteria on the boundedness, global exponential stability and the existence of periodic solutions. In these criteria, we do not require that the response functions are differentiable, bounded and monotone nondecreasing. Some previous works are improved and extended
Muonium hyperfine parameters in Si1-x Ge x alloys

International Nuclear Information System (INIS)

King, Philip; Lichti, Roger; Cottrell, Stephen; Yonenaga, Ichiro

2006-01-01

We present studies of muonium behaviour in bulk, Czochralski-grown Si 1- x Ge x alloy material, focusing in particular on the hyperfine parameter of the tetrahedral muonium species. In contrast to the bond-centred species, the hyperfine parameter of the tetrahedral-site muonium centre (Mu T ) appears to vary non-linearly with alloy composition. The temperature dependence of the Mu T hyperfine parameter observed in low-Ge alloy material is compared with that seen in pure Si, and previous models of the Mu T behaviour in Si are discussed in the light of results from Si 1- x Ge x alloys
THE EVOLUTION OF HETERGENEOUS 'CLUMPY JETS': A PARAMETER STUDY

International Nuclear Information System (INIS)

Yirak, Kristopher; Schroeder, Ed; Frank, Adam; Cunningham, Andrew J.

2012-01-01

We investigate the role discrete clumps embedded in an astrophysical jet play on the jet's morphology and line emission characteristics. By varying clumps' size, density, position, and velocity, we cover a range of parameter space motivated by observations of objects such as the Herbig-Haro object HH 34. We here extend the results presented in Yirak et al., including how analysis of individual observations may lead to spurious sinusoidal variation whose parameters vary widely over time, owing chiefly to interactions between clumps. The goodness of fits, while poor in all simulations, are best when clump-clump collisions are minimal. Our results indicate that a large velocity dispersion leads to a clump-clump collision-dominated flow which disrupts the jet beam. Finally, we present synthetic emission images of Hα and [S II] and note an excess of [S II] emission along the jet length as compared to observations. This suggests that observed beams undergo earlier processing, if they are present at all.
Prediction of Agriculture Drought Using Support Vector Regression Incorporating with Climatology Indices

Science.gov (United States)

Tian, Y.; Xu, Y. P.

2017-12-01

In this paper, the Support Vector Regression (SVR) model incorporating climate indices and drought indices are developed to predict agriculture drought in Xiangjiang River basin, Central China. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). According to the analysis of the relationship between SPEI with different time scales and soil moisture, it is found that SPEI of six months time scales (SPEI-6) could reflect the soil moisture better than that of three and one month time scale from the drought features including drought duration, severity and peak. Climate forcing like El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are represented by climate indices such as MEI and series indices of WPSH. Ridge Point of WPSH is found to be the key factor that influences the agriculture drought mainly through the control of temperature. Based on the climate indices analysis, the predictions of SPEI-6 are conducted using the SVR model. The results show that the SVR model incorperating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that using drought index only. The improvement was more significant for the prediction of one month lead time than that of three months lead time. However, it needs to be cautious in selection of the input parameters, since adding more useless information could have a counter effect in attaining a better prediction.
Novel global robust stability criteria for interval neural networks with multiple time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.

2005-01-01

This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method
Neutron fluctuations in a multiplying medium randomly varying in time

Energy Technology Data Exchange (ETDEWEB)

Pal, L. [KFKI Atomic Energy Research Inst., Budapest (Hungary); Pazsit, I. [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Nuclear Engineering

2006-07-15

The master equation approach, which has traditionally been used for the calculation of neutron fluctuations in multiplying systems with constant parameters, is extended to a case when the parameters of the system change randomly in time. A forward type master equation is considered for the case of a multiplying system whose properties jump randomly between two discrete states, both with and without a stationary external source. The first two factorial moments are calculated, including the covariance. This model can be considered as the unification of stochastic methods that were used either in a constant multiplying medium via the master equation technique, or in a fluctuating medium via the Langevin technique. The results obtained show a much richer characteristic of the zero power noise than that in constant systems. The results are relevant in medium power subcritical nuclear systems where the zero power noise is still significant, but they also have a bearing on all types of branching processes, such as evolution of biological systems, spreading of epidemics etc, which are set in a time-varying environment.

Neutron fluctuations in a multiplying medium randomly varying in time

International Nuclear Information System (INIS)

Pal, L.; Pazsit, I.

2006-01-01

The master equation approach, which has traditionally been used for the calculation of neutron fluctuations in multiplying systems with constant parameters, is extended to a case when the parameters of the system change randomly in time. A forward type master equation is considered for the case of a multiplying system whose properties jump randomly between two discrete states, both with and without a stationary external source. The first two factorial moments are calculated, including the covariance. This model can be considered as the unification of stochastic methods that were used either in a constant multiplying medium via the master equation technique, or in a fluctuating medium via the Langevin technique. The results obtained show a much richer characteristic of the zero power noise than that in constant systems. The results are relevant in medium power subcritical nuclear systems where the zero power noise is still significant, but they also have a bearing on all types of branching processes, such as evolution of biological systems, spreading of epidemics etc, which are set in a time-varying environment
Parametric output-only identification of time-varying structures using a kernel recursive extended least squares TARMA approach

Science.gov (United States)

Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim

2018-01-01

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.
Influence of Some Variable Parameters on Horizontal Elliptic Micro ...

African Journals Online (AJOL)

The study investigates the laminar flow and heat transfer characteristics in elliptic micro-channels of varying axis ratios and with internal longitudinal fins, operating in a region that is hydrodynamically and thermally fully developed; purposely to determine the effects of some salient fluid and geometry parameters such as ...
Kinetic parameter estimation from attenuated SPECT projection measurements

International Nuclear Information System (INIS)

Reutter, B.W.; Gullberg, G.T.

1998-01-01

Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters
Holographic cinematography of time-varying reflecting and time-varying phase objects using a Nd:YAG laser

Science.gov (United States)

Decker, A. J.

1982-01-01

The use of a Nd:YAG laser to record holographic motion pictures of time-varying reflecting objects and time-varying phase objects is discussed. Sample frames from both types of holographic motion pictures are presented. The holographic system discussed is intended for three-dimensional flow visualization of the time-varying flows that occur in jet-engine components.
[Subjective sensations indicating simulator sickness and fatigue after exposure to virtual reality].

Science.gov (United States)

Malińska, Marzena; Zuzewicz, Krystyna; Bugajska, Joanna; Grabowski, Andrzej

2014-01-01

The study assessed the incidence and intensity of subjective symptoms indicating simulator sickness among the persons with no inclination to motion sickness, immersed in virtual reality (VR) by watching an hour long movie in the stereoscopic (three-dimensional - 3D) and non-stereoscopic (two-dimensional - 2D) versions and after an hour long training using virtual reality, called sVR. The sample comprised 20 healthy young men with no inclination to motion sickness. The participants' subjective sensations, indicating symptoms of simulator sickness were assessed using the questionnaire completed by the participants immediately, 20 min and 24 h following the test. Grandjean's scale was used to assess fatigue and mood. The symptoms were observed immediately after the exposure to sVR. Their intensity was higher than after watching the 2D and 3D movies. A significant relationship was found between the eye pain and the type of exposure (2D, 3D and sVR) (Chi2)(2) = 6.225, p < or = 0.05); the relationship between excessive perspiration and the exposure to 31) movie and sVR was also noted (Chi2(1) = 9.173, p < or = 0.01). Some symptoms were still observed 20 min after exposure to sVR. The comparison of Grandjean's scale results before and after the training in sVR handing showed significant differences in 11 out of 14 subscales. Before and after exposure to 3D movie, the differences were significant only for the "tired-fatigued" subscale (Z = 2.501, p < or = 0.012) in favor of "fatigued". Based on the subjective sensation of discomfort after watching 2D and 3D movies it is impossible to predict symptoms of simulator sickness after training using sVR.
Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

Directory of Open Access Journals (Sweden)

S.K. Lahiri

2009-09-01

Full Text Available Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR, a new powerful machine learning methodbased on a statistical learning theory (SLT into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression – genetic algorithm (SVR-GA for modeling and optimization of mono ethylene glycol (MEG quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc. is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.
Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

Science.gov (United States)

Yu, Yaojun

A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.
Impulsive effect on global exponential stability of BAM fuzzy cellular neural networks with time-varying delays

Science.gov (United States)

Li, Kelin

2010-02-01

In this article, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time-varying delays is formulated and investigated. By employing delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time-varying delays are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.
Optimization of process parameters in welding of dissimilar steels using robot TIG welding

Science.gov (United States)

Navaneeswar Reddy, G.; VenkataRamana, M.

2018-03-01

Robot TIG welding is a modern technique used for joining two work pieces with high precision. Design of Experiments is used to conduct experiments by varying weld parameters like current, wire feed and travelling speed. The welding parameters play important role in joining of dissimilar stainless steel SS 304L and SS430. In this work, influences of welding parameter on Robot TIG Welded specimens are investigated using Response Surface Methodology. The Micro Vickers hardness tests of the weldments are measured. The process parameters are optimized to maximize the hardness of the weldments.
Analysis of glottal source parameters in Parkinsonian speech.

Science.gov (United States)

Hanratty, Jane; Deegan, Catherine; Walsh, Mary; Kirkpatrick, Barry

2016-08-01

Diagnosis and monitoring of Parkinson's disease has a number of challenges as there is no definitive biomarker despite the broad range of symptoms. Research is ongoing to produce objective measures that can either diagnose Parkinson's or act as an objective decision support tool. Recent research on speech based measures have demonstrated promising results. This study aims to investigate the characteristics of the glottal source signal in Parkinsonian speech. An experiment is conducted in which a selection of glottal parameters are tested for their ability to discriminate between healthy and Parkinsonian speech. Results for each glottal parameter are presented for a database of 50 healthy speakers and a database of 16 speakers with Parkinsonian speech symptoms. Receiver operating characteristic (ROC) curves were employed to analyse the results and the area under the ROC curve (AUC) values were used to quantify the performance of each glottal parameter. The results indicate that glottal parameters can be used to discriminate between healthy and Parkinsonian speech, although results varied for each parameter tested. For the task of separating healthy and Parkinsonian speech, 2 out of the 7 glottal parameters tested produced AUC values of over 0.9.
Application of function-oriented roughness parameters using confocal microscopy

Directory of Open Access Journals (Sweden)

K. Klauer

2018-06-01

Full Text Available Optical measuring instruments are widely used for the functional characterization of surface topography. However, due to the interaction of the surface with the incident light, effects occur that can influence the measured topography height values and the obtained surface texture parameters. Therefore, we describe a systematic investigation of the influences of optical surface topography measurement on the acquisition of function-oriented roughness parameters. The same evaluation areas of varying cylinder liners which represent a typical application of function-oriented roughness parameters were measured with a confocal microscope and a stylus instrument. Functional surface texture parameters as given in the standards ISO 13565–2, ISO 13565–3 and ISO 25178–2 were evaluated for both measurement methods and compared. The transmission of specific surface features was described and a correlation analysis for the surface topographies obtained with the different measurement methods and their resulting functional roughness parameters was carried out. Keywords: Functional surface characterization, Optical metrology, Topography measurement, Roughness
Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model

Science.gov (United States)

Cheong, Chin Wen

2008-02-01

This article investigated the influences of structural breaks on the fractionally integrated time-varying volatility model in the Malaysian stock markets which included the Kuala Lumpur composite index and four major sectoral indices. A fractionally integrated time-varying volatility model combined with sudden changes is developed to study the possibility of structural change in the empirical data sets. Our empirical results showed substantial reduction in fractional differencing parameters after the inclusion of structural change during the Asian financial and currency crises. Moreover, the fractionally integrated model with sudden change in volatility performed better in the estimation and specification evaluations.
Approximate P-wave ray tracing and dynamic ray tracing in weakly orthorhombic media of varying symmetry orientation

KAUST Repository

Masmoudi, Nabil; Pšenčí k, Ivan

2014-01-01

We present an approximate, but efficient and sufficiently accurate P-wave ray tracing and dynamic ray tracing procedure for 3D inhomogeneous, weakly orthorhombic media with varying orientation of symmetry planes. In contrast to commonly used approaches, the orthorhombic symmetry is preserved at any point of the model. The model is described by six weak-anisotropy parameters and three Euler angles, which may vary arbitrarily, but smoothly, throughout the model. We use the procedure for the calculation of rays and corresponding two-point traveltimes in a VSP experiment in a part of the BP benchmark model generalized to orthorhombic symmetry.
Modelling of coupled heat and electric field distribution during ohmic heating of solid foods with varying sizes

DEFF Research Database (Denmark)

Feyissa, Aberham Hailu; Bøknæs, Niels; Nielsen, P.L.

factors leading to variations and uncertainties in prediction of the right process parameters. The current work is focused on modelling of OH of solid food pieces of varying sizes cooked in one batch. A 3D mathematical model of coupled heat transfer and electric field during OH of shrimps has been...
Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2011-10-31

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain
Hydrodynamic mean-field solutions of 1D exclusion processes with spatially varying hopping rates

Energy Technology Data Exchange (ETDEWEB)

Lakatos, Greg; O' Brien, John; Chou, Tom [Department of Biomathematics and Institute for Pure and Applied Mathematics, UCLA, Los Angeles, CA 90095 (United States)

2006-03-10

We analyse the open boundary partially asymmetric exclusion process with smoothly varying internal hopping rates in the infinite-size, mean-field limit. The mean-field equations for particle densities are written in terms of Ricatti equations with the steady-state current J as a parameter. These equations are solved both analytically and numerically. Upon imposing the boundary conditions set by the injection and extraction rates, the currents J are found self-consistently. We find a number of cases where analytic solutions can be found exactly or approximated. Results for J from asymptotic analyses for slowly varying hopping rates agree extremely well with those from extensive Monte Carlo simulations, suggesting that mean-field currents asymptotically approach the exact currents in the hydrodynamic limit, as the hopping rates vary slowly over the lattice. If the forward hopping rate is greater than or less than the backward hopping rate throughout the entire chain, the three standard steady-state phases are preserved. Our analysis reveals the sensitivity of the current to the relative phase between the forward and backward hopping rate functions.
Hydrodynamic mean-field solutions of 1D exclusion processes with spatially varying hopping rates

International Nuclear Information System (INIS)

Lakatos, Greg; O'Brien, John; Chou, Tom

2006-01-01

We analyse the open boundary partially asymmetric exclusion process with smoothly varying internal hopping rates in the infinite-size, mean-field limit. The mean-field equations for particle densities are written in terms of Ricatti equations with the steady-state current J as a parameter. These equations are solved both analytically and numerically. Upon imposing the boundary conditions set by the injection and extraction rates, the currents J are found self-consistently. We find a number of cases where analytic solutions can be found exactly or approximated. Results for J from asymptotic analyses for slowly varying hopping rates agree extremely well with those from extensive Monte Carlo simulations, suggesting that mean-field currents asymptotically approach the exact currents in the hydrodynamic limit, as the hopping rates vary slowly over the lattice. If the forward hopping rate is greater than or less than the backward hopping rate throughout the entire chain, the three standard steady-state phases are preserved. Our analysis reveals the sensitivity of the current to the relative phase between the forward and backward hopping rate functions
Global and nonglobal parameters of horizontal-branch morphology of globular clusters

International Nuclear Information System (INIS)

Milone, A. P.; Marino, A. F.; Dotter, A.; Norris, J. E.; Jerjen, H.; Asplund, M.

2014-01-01

The horizontal-branch (HB) morphology of globular clusters (GCs) is mainly determined by metallicity. However, the fact that GCs with almost the same metallicity exhibit different HB morphologies demonstrates that at least one more parameter is needed to explain the HB morphology. It has been suggested that one of these should be a global parameter that varies from GC to GC and the other a nonglobal parameter that varies within the GC. In this study we provide empirical evidence corroborating this idea. We used the photometric catalogs obtained with the Advanced Camera for Surveys of the Hubble Space Telescope and analyze the color-magnitude diagrams of 74 GCs. The HB morphology of our sample of GCs has been investigated on the basis of the two new parameters L1 and L2 that measure the distance between the red giant branch and the coolest part of the HB and the color extension of the HB, respectively. We find that L1 correlates with both metallicity and age, whereas L2 most strongly correlates with the mass of the hosting GC. The range of helium abundance among the stars in a GC, characterized by ΔY and associated with the presence of multiple stellar populations, has been estimated in a few GCs to date. In these GCs we find a close relationship among ΔY, GC mass, and L2. We conclude that age and metallicity are the main global parameters, while the range of helium abundance within a GC is the main nonglobal parameter defining the HB morphology of Galactic GCs.
Laser Trimming of CuAlMo Thin-Film Resistors: Effect of Laser Processing Parameters

Science.gov (United States)

Birkett, Martin; Penlington, Roger

2012-08-01

This paper reports the effect of varying laser trimming process parameters on the electrical performance of a novel CuAlMo thin-film resistor material. The films were prepared on Al2O3 substrates by direct-current (DC) magnetron sputtering, before being laser trimmed to target resistance value. The effect of varying key laser parameters of power, Q-rate, and bite size on the resistor stability and tolerance accuracy were systematically investigated. By reducing laser power and bite size and balancing this with Q-rate setting, significant improvements in resistor stability and resistor tolerance accuracies of less than ±0.5% were achieved.

Effect of laser pulse shaping parameters on the fidelity of quantum logic gates.

Science.gov (United States)

Zaari, Ryan R; Brown, Alex

2012-09-14

The effect of varying parameters specific to laser pulse shaping instruments on resulting fidelities for the ACNOT(1), NOT(2), and Hadamard(2) quantum logic gates are studied for the diatomic molecule (12)C(16)O. These parameters include varying the frequency resolution, adjusting the number of frequency components and also varying the amplitude and phase at each frequency component. A time domain analytic form of the original discretized frequency domain laser pulse function is derived, providing a useful means to infer the resulting pulse shape through variations to the aforementioned parameters. We show that amplitude variation at each frequency component is a crucial requirement for optimal laser pulse shaping, whereas phase variation provides minimal contribution. We also show that high fidelity laser pulses are dependent upon the frequency resolution and increasing the number of frequency components provides only a small incremental improvement to quantum gate fidelity. Analysis through use of the pulse area theorem confirms the resulting population dynamics for one or two frequency high fidelity laser pulses and implies similar dynamics for more complex laser pulse shapes. The ability to produce high fidelity laser pulses that provide both population control and global phase alignment is attributed greatly to the natural evolution phase alignment of the qubits involved within the quantum logic gate operation.
Energy spectrum of two-dimensional tight-binding electrons in a spatially varying magnetic field

International Nuclear Information System (INIS)

Oh, G.Y.; Lee, M.H.

1996-01-01

The electronic energy spectrum of a two-dimensional lattice in a spatially varying magnetic field is studied within the framework of the tight-binding model by using the scheme of the transfer matrix. It is found that, in comparison with the case of a uniform magnetic field, the energy spectrum exhibits more complicated behavior; band broadening (or gap closing) and band splitting (or gap opening) occur depending on characteristic parameters of the lattice. The origin of these phenomena lies in the existence of direct touching and indirect overlapping between neighboring subbands. Dependence of direct touching and indirect overlapping, and thus the electronic band structure together with the density of states, on characteristic parameters of the lattice is elucidated in detail. copyright 1996 The American Physical Society
Program for searching for semiempirical parameters by the MNDO method

International Nuclear Information System (INIS)

Bliznyuk, A.A.; Voityuk, A.A.

1987-01-01

The authors describe an program for optimizing atomic models constructed using the MNDO method which varies not only the parameters but also the scope for simple changes in the calculation scheme. The target function determines properties such as formation enthalpies, dipole moments, ionization potentials, and geometrical parameters. Software used to minimize the target function is based on the simplex method on the Nelder-Mead algorithm and on the Fletcher variable-metric method. The program is written in FORTRAN IV and implemented on the ES computer
Process parameters optimization of needle-punched nonwovens for sound absorption application

CSIR Research Space (South Africa)

Mvubu, M

2015-12-01

Full Text Available , and stroke frequency on sound absorption properties were studied. These parameters were varied at three levels during experimental trials. From multiple regression analysis, it was observed that the depth of needle penetration alone was the most dominant...
Investigation on gas medium parameters for an ArF excimer laser through orthogonal experimental design

Science.gov (United States)

Song, Xingliang; Sha, Pengfei; Fan, Yuanyuan; Jiang, R.; Zhao, Jiangshan; Zhou, Yi; Yang, Junhong; Xiong, Guangliang; Wang, Yu

2018-02-01

Due to complex kinetics of formation and loss mechanisms, such as ion-ion recombination reaction, neutral species harpoon reaction, excited state quenching and photon absorption, as well as their interactions, the performance behavior of different laser gas medium parameters for excimer laser varies greatly. Therefore, the effects of gas composition and total gas pressure on excimer laser performance attract continual research studies. In this work, orthogonal experimental design (OED) is used to investigate quantitative and qualitative correlations between output laser energy characteristics and gas medium parameters for an ArF excimer laser with plano-plano optical resonator operation. Optimized output laser energy with good pulse to pulse stability can be obtained effectively by proper selection of the gas medium parameters, which makes the most of the ArF excimer laser device. Simple and efficient method for gas medium optimization is proposed and demonstrated experimentally, which provides a global and systematic solution. By detailed statistical analysis, the significance sequence of relevant parameter factors and the optimized composition for gas medium parameters are obtained. Compared with conventional route of varying single gas parameter factor sequentially, this paper presents a more comprehensive way of considering multivariables simultaneously, which seems promising in striking an appropriate balance among various complicated parameters for power scaling study of an excimer laser.
A varying-α brane world cosmology

International Nuclear Information System (INIS)

Youm, Donam

2001-08-01

We study the brane world cosmology in the RS2 model where the electric charge varies with time in the manner described by the varying fine-structure constant theory of Bekenstein. We map such varying electric charge cosmology to the dual variable-speed-of-light cosmology by changing system of units. We comment on cosmological implications for such cosmological models. (author)
Hepatitis C treatment among racial and ethnic groups in the IDEAL trial.

Science.gov (United States)

Muir, A J; Hu, K-Q; Gordon, S C; Koury, K; Boparai, N; Noviello, S; Albrecht, J K; Sulkowski, M S; McCone, J

2011-04-01

Previous studies of chronic hepatitis C virus (HCV) treatment have demonstrated variations in response among racial and ethnic groups including poorer efficacy rates among African American and Hispanic patients. The individualized dosing efficacy vs flat dosing to assess optimaL pegylated interferon therapy (IDEAL) trial enrolled 3070 patients from 118 United States centres to compare treatment with peginterferon (PEG-IFN) alfa-2a and ribavirin (RBV) and two doses of PEG-IFN alfa-2b and RBV. This analysis examines treatment response among the major racial and ethnic groups in the trial. Overall, sustained virologic response (SVR) rates were 44% for white, 22% for African American, 38% for Hispanic and 59% for Asian American patients. For patients with undetectable HCV RNA at treatment week 4, the positive predictive value of SVR was 86% for white, 92% for African American, 83% for Hispanic and 89% for Asian American patients. The positive predictive values of SVR in those with undetectable HCV RNA at treatment week 12 ranged from 72% to 81%. Multivariate regression analysis using baseline characteristics demonstrated that treatment regimen was not a predictor of SVR. Despite wide-ranging SVR rates among the different racial and ethnic groups, white and Hispanic patients had similar SVR rates. In all groups, treatment response was largely determined by antiviral activity in the first 12 weeks of treatment. Therefore, decisions regarding HCV treatment should consider the predictive value of the early on-treatment response, not just baseline characteristics, such as race and ethnicity. © 2010 Blackwell Publishing Ltd.
Real-world efficacy and safety of ritonavir-boosted paritaprevir, ombitasvir, dasabuvir ± ribavirin for hepatitis C genotype 1 - final results of the REV1TAL study.

Science.gov (United States)

Lubel, John; Strasser, Simone; Stuart, Katherine A; Dore, Gregory; Thompson, Alexander; Pianko, Stephen; Bollipo, Steven; Mitchell, Joanne L; Fragomeli, Vincenzo; Jones, Tracey; Chivers, Sarah; Gow, Paul; Iser, David; Levy, Miriam; Tse, Edmund; Gazzola, Alessia; Cheng, Wendy; Nazareth, Saroj; Galhenage, Sam; Wade, Amanda; Weltman, Martin; Wigg, Alan; MacQuillan, Gerry; Sasadeusz, Joe; George, Jacob; Zekry, Amany; Roberts, Stuart K

2017-01-01

Limited data exist on the outcomes of ritonavir-boosted paritaprevir with ombitasvir and dasabuvir (PrOD) ± ribavirin in a real-world setting. The aim of this study was to compare the efficacy and safety of PrOD-based therapy in hepatitis C genotype 1 patients with and without cirrhosis, and to explore pre-treatment factors predictive of sustained viral response (SVR) and serious adverse events (SAEs) on treatment. 451 patients with hepatitis C genotype 1 treated in 20 centres across Australia were included. Baseline demographic, clinical and laboratory information, on-treatment biochemical, virological and haematological indices and details on serious adverse events were collected locally. Cirrhosis was present in 340 patients (75.4%). Overall SVR was 95.1% with no differences in SVR between the cirrhosis and non-cirrhosis groups (94.7% versus 96.4%). SVR in subgenotypes 1a and 1b was 93.1% and 99.2%, respectively. On multivariate analysis, baseline bilirubin level and early treatment cessation predicted SVR. SAEs occurred in 10.9% of patients including hepatic decompensation (2.7%) and hepatocellular carcinoma (1.8%). On multivariate analysis of factors predictive of SAEs in the overall group, Child-Turcotte-Pugh (CTP) B was the only significant factor, while in those with cirrhosis, baseline albumin and creatinine levels were significant. In this large real-world cohort of HCV genotype 1 subjects, treatment with PrOD was highly effective and similar to clinical trials. Important determinants of reduced SVR include early cessation of therapy and baseline bilirubin concentration. SAEs were not infrequent with CTP B patients being at greatest risk.
Effects of varying environmental conditions on emissivity spectra of bulk lunar soils: Application to Diviner thermal infrared observations of the Moon

Science.gov (United States)

Donaldson Hanna, K. L.; Greenhagen, B. T.; Patterson, W. R.; Pieters, C. M.; Mustard, J. F.; Bowles, N. E.; Paige, D. A.; Glotch, T. D.; Thompson, C.

2017-02-01

Currently, few thermal infrared measurements exist of fine particulate (samples (e.g. minerals, mineral mixtures, rocks, meteorites, and lunar soils) measured under simulated lunar conditions. Such measurements are fundamental for interpreting thermal infrared (TIR) observations by the Diviner Lunar Radiometer Experiment (Diviner) onboard NASA's Lunar Reconnaissance Orbiter as well as future TIR observations of the Moon and other airless bodies. In this work, we present thermal infrared emissivity measurements of a suite of well-characterized Apollo lunar soils and a fine particulate (sample as we systematically vary parameters that control the near-surface environment in our vacuum chamber (atmospheric pressure, incident solar-like radiation, and sample cup temperature). The atmospheric pressure is varied between ambient (1000 mbar) and vacuum (radiation is varied between 52 and 146 mW/cm2, and the sample cup temperature is varied between 325 and 405 K. Spectral changes are characterized as each parameter is varied, which highlight the sensitivity of thermal infrared emissivity spectra to the atmospheric pressure and the incident solar-like radiation. Finally spectral measurements of Apollo 15 and 16 bulk lunar soils are compared with Diviner thermal infrared observations of the Apollo 15 and 16 sampling sites. This comparison allows us to constrain the temperature and pressure conditions that best simulate the near-surface environment of the Moon for future laboratory measurements and to better interpret lunar surface compositions as observed by Diviner.
Hemodynamic changes after levothyroxine treatment in subclinical hypothyroidism

DEFF Research Database (Denmark)

Faber, J; Petersen, L; Wiinberg, N

2002-01-01

by LT(4) (p treatment in SH results in changes in hemodynamic parameters of potentially beneficial character. SH and overt hypothyroidism should......In hypothyroidism, lack of thyroid hormones results in reduced cardiac function (cardiac output [CO]), and an increase of systemic vascular resistance (SVR). We speculated whether hemodynamic regulation in subjects with subclinical hypothyroidism (SH) (defined as mildly elevated thyrotropin [TSH......) and T(3) estimates) LT(4) treatment resulted in 6% reduction in supine MAP (p treatment (p
Tracking control of time-varying knee exoskeleton disturbed by interaction torque.

Science.gov (United States)

Li, Zhan; Ma, Wenhao; Yin, Ziguang; Guo, Hongliang

2017-11-01

Knee exoskeletons have been increasingly applied as assistive devices to help lower-extremity impaired people to make their knee joints move through providing external movement compensation. Tracking control of knee exoskeletons guided by human intentions often encounters time-varying (time-dependent) issues and the disturbance interaction torque, which may dramatically put an influence up on their dynamic behaviors. Inertial and viscous parameters of knee exoskeletons can be estimated to be time-varying due to unexpected mechanical vibrations and contact interactions. Moreover, the interaction torque produced from knee joint of wearers has an evident disturbance effect on regular motions of knee exoskeleton. All of these points can increase difficultly of accurate control of knee exoskeletons to follow desired joint angle trajectories. This paper proposes a novel control strategy for controlling knee exoskeleton with time-varying inertial and viscous coefficients disturbed by interaction torque. Such designed controller is able to make the tracking error of joint angle of knee exoskeletons exponentially converge to zero. Meanwhile, the proposed approach is robust to guarantee the tracking error bounded when the interaction torque exists. Illustrative simulation and experiment results are presented to show efficiency of the proposed controller. Additionally, comparisons with gradient dynamic (GD) approach and other methods are also presented to demonstrate efficiency and superiority of the proposed control strategy for tracking joint angle of knee exoskeleton. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

Science.gov (United States)

Ichii, Kazuhito; Ueyama, Masahito; Kondo, Masayuki; Saigusa, Nobuko; Kim, Joon; Alberto, Ma. Carmelita; Ardö, Jonas; Euskirchen, Eugénie S.; Kang, Minseok; Hirano, Takashi; Joiner, Joanna; Kobayashi, Hideki; Marchesini, Luca Belelli; Merbold, Lutz; Miyata, Akira; Saitoh, Taku M.; Takagi, Kentaro; Varlagin, Andrej; Bret-Harte, M. Syndonia; Kitamura, Kenzo; Kosugi, Yoshiko; Kotani, Ayumi; Kumar, Kireet; Li, Sheng-Gong; Machimura, Takashi; Matsuura, Yojiro; Mizoguchi, Yasuko; Ohta, Takeshi; Mukherjee, Sandipan; Yanagi, Yuji; Yasuda, Yukio; Zhang, Yiping; Zhao, Fenghua

2017-04-01

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r2 = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2 = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
Comparison of results from simple expressions for MOSFET parameter extraction

Science.gov (United States)

Buehler, M. G.; Lin, Y.-S.

1988-01-01

In this paper results are compared from a parameter extraction procedure applied to the linear, saturation, and subthreshold regions for enhancement-mode MOSFETs fabricated in a 3-micron CMOS process. The results indicate that the extracted parameters differ significantly depending on the extraction algorithm and the distribution of I-V data points. It was observed that KP values vary by 30 percent, VT values differ by 50 mV, and Delta L values differ by 1 micron. Thus for acceptance of wafers from foundries and for modeling purposes, the extraction method and data point distribution must be specified. In this paper measurement and extraction procedures that will allow a consistent evaluation of measured parameters are discussed.
Impact of donor and recipient single nucleotide polymorphisms of IL28B rs8099917 in living donor liver transplantation for hepatitis C.

Directory of Open Access Journals (Sweden)

Nobuhiro Harada

Full Text Available Single nucleotide polymorphisms of interleukin-28B (IL28B rs8099917 are reported to be associated with virologic clearance in interferon-and ribavirin -based treatment for hepatitis C virus (HCV-infected patients. We examined virologic response in accordance with IL28B polymorphisms in our living donor liver transplantation series under a preemptive interferon and RBV treatment approach. Adequate DNA samples from both the recipient and donor for the study of single nucleotide polymorphisms of IL28B were available from 96 cases and were the subjects of the present study. Various clinical factors related with virologic response including early virologic response (EVR and sustained virologic response (SVR were examined. Totally 51% presented with EVR and 44% achieved SVR. Presence of the major allele (TT in either the recipient or the donor corresponded to SVR of 53% and 48%. Presence of the minor allele (TG or GG corresponded to SVR of 26% and 32%. Multivariate analysis revealed that genotype of HCV or EVR, but not IL28B polymorphisms in either the recipient or donor, was an independent factor for achieving SVR. When virologic response to treatment was incorporated into analysis, the impact of IL28B polymorphism on virological clearance remained relative to other factors and was not significantly independent.
Faraday tarotion: new parameter for electromagnetic pulse propagation in magnetoplasma

International Nuclear Information System (INIS)

Bloch, S.C.; Lyons, P.W.

1976-01-01

Extreme distortion and time-dependent Faraday rotation occur for propagation of short electromagnetic pulses in magnetoplasma, for some ranges of plasma parameters. In order to relate pulse and monochromatic waves for propagation-path diagnostic purposes, a new parameter is introduced for the transmitted pulse train which has properties that correspond very accurately to results that would be expected for Faraday rotation of a continuous wave having the central frequency of the incident pulse spectrum. Results for 5-ns pulses (10 GHz) are presented for varying propagating length, static magnetic field, electron density, and collisional absorption
Varying parameter models to accommodate dynamic promotion effects

NARCIS (Netherlands)

Foekens, E.W.; Leeflang, P.S.H.; Wittink, D.R.

1999-01-01

The purpose of this paper is to examine the dynamic effects of sales promotions. We create dynamic brand sales models (for weekly store-level scanner data) by relating store intercepts and a brand's own price elasticity to a measure of the cumulated previous price discounts - amount and time - for
Structured Control of Affine Linear Parameter Varying Systems

DEFF Research Database (Denmark)

Adegas, Fabiano Daher; Stoustrup, Jakob

2011-01-01

This paper presents a new procedure to design structured controllers for discrete-time afﬁne linear parametervarying systems (A LPV). The class of control structures includes decentralized of any order, ﬁxed order output feedback, simultaneous plant-control design, among others. A parametervarying...... non-convex condition for an upper bound on the induced L2-norm performance is solved by an iterative linear matrix inequalities (LMI) optimization algorithm. Numerical examples demostrate the effectiveness of the proposed approach....
Lower liver stiffness in patients with sustained virological response 4 years after treatment for chronic hepatitis C

DEFF Research Database (Denmark)

Andersen, Ellen Sloth; Moessner, Belinda Klemmensen; Christensen, Peer Brehm

2011-01-01

Transient elastography (TE) is a noninvasive and well validated method for measurement of liver stiffness. The aim of this study was to use TE to evaluate whether patients with sustained virological response (SVR) have lower liver stiffness than patients with non-SVR after treatment for chronic...
Skin friction measurements of systematically-varied roughness: Probing the role of roughness amplitude and skewness

Science.gov (United States)

Barros, Julio; Flack, Karen; Schultz, Michael

2017-11-01

Real-world engineering systems which feature either external or internal wall-bounded turbulent flow are routinely affected by surface roughness. This gives rise to performance degradation in the form of increased drag or head loss. However, at present there is no reliable means to predict these performance losses based upon the roughness topography alone. This work takes a systematic approach by generating random surface roughness in which the surface statistics are closely controlled. Skin friction and roughness function results will be presented for two groups of these rough surfaces. The first group is Gaussian (i.e. zero skewness) in which the root-mean-square roughness height (krms) is varied. The second group has a fixed krms, and the skewness is varied from approximately -1 to +1. The effect of the roughness amplitude and skewness on the skin friction will be discussed. Particular attention will be paid to the effect of these parameters on the roughness function in the transitionally-rough flow regime. For example, the role these parameters play in the monotonic or inflectional nature of the roughness function will be addressed. Future research into the details of the turbulence structure over these rough surfaces will also be outlined. Research funded by U.S. Office of Naval Research (ONR).
Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set

OpenAIRE

Bouman, HA; Platt, T; Doblin, M; Figueiras, FG; Gudmundsson, K; Gudfinnsson, HG; Huang, B; Hickman, A; Hiscock, M; Jackson, T; Lutz, VA; Melin, F; Rey, F; Pepin, P; Segura, V

2018-01-01

The photosynthetic performance of marine phytoplankton varies in response to a variety of factors, environmental and taxonomic. One of the aims of the MArine primary Production: model Parameters from Space (MAPPS) project of the European Space Agency is to assemble a global database of photosynthesis–irradiance (P-E) parameters from a range of oceanographic regimes as an aid to examining the basin-scale variability in the photophysiological response of marine phytoplankto...

Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

Directory of Open Access Journals (Sweden)

Changwei Ma

2015-01-01

Full Text Available Discrete Fourier transform- (DFT- based maximum likelihood (ML algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB, which is dependent on the number of DFT points. However, its mean square error (MSE performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR, least squares SVR (LS-SVR can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.
Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

Directory of Open Access Journals (Sweden)

Xian-Xia Zhang

2013-01-01

Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.
Endothelial Function as a Possible Significant Determinant of Cardiac Function during Exercise in Patients with Structural Heart Disease

Directory of Open Access Journals (Sweden)

Bonpei Takase

2009-01-01

Full Text Available This study was investigated the role that endothelial function and systemic vascular resistance (SVR play in determining cardiac function reserve during exercise by a new ambulatory radionuclide monitoring system (VEST in patients with heart disease. The study population consisted of 32 patients. The patients had cardiopulmonary stress testing using the treadmill Ramp protocol and the VEST. The anaerobic threshold (AT was autodetermined using the V-slope method. The SVR was calculated by determining the mean blood pressure/cardiac output. Flow-mediated vasodilation (FMD was measured in the brachial artery to evaluate endotheilial function. FMD and the percent change f'rom rest to AT in SVR correlated with those from rest to AT in ejection fraction and peak ejection ratio by VEST, respectively. Our findings suggest that FMD in the brachial artery and the SVR determined by VEST in patients with heart disease can possibly reflect cardiac function reserve during aerobic exercise.
Linking the fractional derivative and the Lomnitz creep law to non-Newtonian time-varying viscosity

Science.gov (United States)

Pandey, Vikash; Holm, Sverre

2016-09-01

Many of the most interesting complex media are non-Newtonian and exhibit time-dependent behavior of thixotropy and rheopecty. They may also have temporal responses described by power laws. The material behavior is represented by the relaxation modulus and the creep compliance. On the one hand, it is shown that in the special case of a Maxwell model characterized by a linearly time-varying viscosity, the medium's relaxation modulus is a power law which is similar to that of a fractional derivative element often called a springpot. On the other hand, the creep compliance of the time-varying Maxwell model is identified as Lomnitz's logarithmic creep law, making this possibly its first direct derivation. In this way both fractional derivatives and Lomnitz's creep law are linked to time-varying viscosity. A mechanism which yields fractional viscoelasticity and logarithmic creep behavior has therefore been found. Further, as a result of this linking, the curve-fitting parameters involved in the fractional viscoelastic modeling, and the Lomnitz law gain physical interpretation.
Circulating sCD14 is associated with virological response to pegylated-interferon-alpha/ribavirin treatment in HIV/HCV co-infected patients.

Directory of Open Access Journals (Sweden)

Giulia Marchetti

Full Text Available Microbial translocation (MT through the gut accounts for immune activation and CD4+ loss in HIV and may influence HCV disease progression in HIV/HCV co-infection. We asked whether increased MT and immune activation may hamper anti-HCV response in HIV/HCV patients.98 HIV/HCV patients who received pegylated-alpha-interferon (peg-INF-alpha/ribavirin were retrospectively analyzed. Baseline MT (lipopolysaccharide, LPS, host response to MT (sCD14, CD38+HLA-DR+CD4+/CD8+, HCV genotype, severity of liver disease were assessed according to Early Virological Response (EVR: HCV-RNA <50 IU/mL at week 12 of therapy or ≥2 log(10 reduction from baseline after 12 weeks of therapy and Sustained Virological Response (SVR: HCV-RNA <50 IU/mL 24 weeks after end of therapy. Mann-Whitney/Chi-square test and Pearson's correlation were used. Multivariable regression was performed to determine factors associated with EVR/SVR.71 patients displayed EVR; 41 SVR. Patients with HCV genotypes 1-4 and cirrhosis presented a trend to higher sCD14, compared to patients with genotypes 2-3 (p = 0.053 and no cirrhosis (p = 0.052. EVR and SVR patients showed lower levels of circulating sCD14 (p = 0.0001, p = 0.026, respectively, but similar T-cell activation compared to Non-EVR (Null Responders, NR and Non-SVR (N-SVR subjects. sCD14 resulted the main predictive factor of EVR (0.145 for each sCD14 unit more, 95%CI 0.031-0.688, p = 0.015. SVR was associated only with HCV genotypes 2-3 (AOR 0.022 for genotypes 1-4 vs 2-3, 95%CI 0.001-0.469, p = 0.014.In HIV/HCV patients sCD14 correlates with the severity of liver disease and predicts early response to peg-INF-alpha/ribavirin, suggesting MT-driven immune activation as pathway of HIV/HCV co-infection and response to therapy.
Predictive value of early viriological response for sustained viriological response in chronic hepatitis c with conventional interferon therapy

International Nuclear Information System (INIS)

Awan, A.; Umar, M.; Khaar, H.T.B.; Kulsoom, A.; Minhas, Z.; Ambreen, S.; Habib, N.; Mumtaz, W.; Habib, F.

2016-01-01

Background: Hepatitis is a major public health problem in Pakistan due to its strong association with liver failure and hepatocellular carcinoma. In Pakistan, conventional interferon therapy along with Ribavirin is favoured especially in Government funded programs for treatment of Hepatitis C, over the more expensive Pegylated Interferon and Ribavirin combination therapy as recommended by Pakistan society of Gastroenterology and GI endoscopy due to its favourable results observed in genotype 3 which is the dominant genotype of this region. Objective of our study was to assess the viriological responses with standard interferon therapy and to determine the predictive values of early viriological response (EVR) for Sustained Viriological Response (SVR) in chronic hepatitis C patients treated with standard interferon therapy. Methods: A cross sectional study was conducted on patients with chronic hepatitis C having received standard interferon and ribavirin therapy for six months. EVR and SVR were noted for analysis. Positive and negative predictive values of EVR on SVR were calculated. Results: Out of the total sample (N=3075), 1946 (63.3 percentage) patients were tested for EVR. 1386 (71.2 percentage) were positive while 560 (28.8 percentage) were negative while 516 (16.8 percentage) were tested for SVR. Two hundred and eighty-five (55.2 percentage) were positive while 231 (44.8 percentage) were negative. EVR and SVR tested were N=117. Positive predictive value of EVR on SVR was 67.1 percentage and negative predictive value was 65.8 percentage. Statistically significant association between EVR and SVR was determined with Chi square statistic of 11.8 (p-value <0.0001). Conclusion: EVR is a good predictor of response of patients to standard interferon and ribavirin therapy. In the absence of an EVR, it seems imperative to stop further treatment. Virilogical responses with conventional interferon therapy are comparable to those of pegylated interferon therapy so
Effect of tool shape and welding parameters on mechanical properties and microstructure of dissimilar friction stir welded aluminium alloys

OpenAIRE

Chetan Aneja; Amit Handa

2016-01-01

In the present experimental study, dissimilar aluminum alloy AA5083 and AA6082 were friction stir welded by varying tool shape, welding speed and rotary speed of the tool in order to investigate the effect of varying tool shape and welding parameters on the mechanical properties as well as microstructure. The friction stir welding (FSW) process parameters have great influence on heat input per unit length of weld. The outcomes of experimental study prove that mechanical properties increases w...
Robust Stabilization of Discrete-Time Systems with Time-Varying Delay: An LMI Approach

Directory of Open Access Journals (Sweden)

Valter J. S. Leite

2008-01-01

Full Text Available Sufficient linear matrix inequality (LMI conditions to verify the robust stability and to design robust state feedback gains for the class of linear discrete-time systems with time-varying delay and polytopic uncertainties are presented. The conditions are obtained through parameter-dependent Lyapunov-Krasovskii functionals and use some extra variables, which yield less conservative LMI conditions. Both problems, robust stability analysis and robust synthesis, are formulated as convex problems where all system matrices can be affected by uncertainty. Some numerical examples are presented to illustrate the advantages of the proposed LMI conditions.
Transport methods: general. 2. Monte Carlo Particle Transport in Media with Exponentially Varying Time-Dependent Cross Sections

International Nuclear Information System (INIS)

Brown, Forrest B.; Martin, William R.

2001-01-01

We have investigated Monte Carlo schemes for analyzing particle transport through media with exponentially varying time-dependent cross sections. For such media, the cross sections are represented in the form Σ(t) = Σ 0 e -at (1) or equivalently as Σ(x) = Σ 0 e -bx (2) where b = av and v is the particle speed. For the following discussion, the parameters a and b may be either positive, for exponentially decreasing cross sections, or negative, for exponentially increasing cross sections. For most time-dependent Monte Carlo applications, the time and spatial variations of the cross-section data are handled by means of a stepwise procedure, holding the cross sections constant for each region over a small time interval Δt, performing the Monte Carlo random walk over the interval Δt, updating the cross sections, and then repeating for a series of time intervals. Continuously varying spatial- or time-dependent cross sections can be treated in a rigorous Monte Carlo fashion using delta-tracking, but inefficiencies may arise if the range of cross-section variation is large. In this paper, we present a new method for sampling collision distances directly for cross sections that vary exponentially in space or time. The method is exact and efficient and has direct application to Monte Carlo radiation transport methods. To verify that the probability density function (PDF) is correct and that the random-sampling procedure yields correct results, numerical experiments were performed using a one-dimensional Monte Carlo code. The physical problem consisted of a beam source impinging on a purely absorbing infinite slab, with a slab thickness of 1 cm and Σ 0 = 1 cm -1 . Monte Carlo calculations with 10 000 particles were run for a range of the exponential parameter b from -5 to +20 cm -1 . Two separate Monte Carlo calculations were run for each choice of b, a continuously varying case using the random-sampling procedures described earlier, and a 'conventional' case where the
Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology.

Directory of Open Access Journals (Sweden)

Steffi Kopprasch

Full Text Available Glucolipotoxicity is a major pathophysiological mechanism in the development of insulin resistance and type 2 diabetes mellitus (T2D. We aimed to detect subtle changes in the circulating lipid profile by shotgun lipidomics analyses and to associate them with four different insulin sensitivity indices.The cross-sectional study comprised 90 men with a broad range of insulin sensitivity including normal glucose tolerance (NGT, n = 33, impaired glucose tolerance (IGT, n = 32 and newly detected T2D (n = 25. Prior to oral glucose challenge plasma was obtained and quantitatively analyzed for 198 lipid molecular species from 13 different lipid classes including triacylglycerls (TAGs, phosphatidylcholine plasmalogen/ether (PC O-s, sphingomyelins (SMs, and lysophosphatidylcholines (LPCs. To identify a lipidomic signature of individual insulin sensitivity we applied three data mining approaches, namely least absolute shrinkage and selection operator (LASSO, Support Vector Regression (SVR and Random Forests (RF for the following insulin sensitivity indices: homeostasis model of insulin resistance (HOMA-IR, glucose insulin sensitivity index (GSI, insulin sensitivity index (ISI, and disposition index (DI. The LASSO procedure offers a high prediction accuracy and and an easier interpretability than SVR and RF.After LASSO selection, the plasma lipidome explained 3% (DI to maximal 53% (HOMA-IR variability of the sensitivity indexes. Among the lipid species with the highest positive LASSO regression coefficient were TAG 54:2 (HOMA-IR, PC O- 32:0 (GSI, and SM 40:3:1 (ISI. The highest negative regression coefficient was obtained for LPC 22:5 (HOMA-IR, TAG 51:1 (GSI, and TAG 58:6 (ISI.Although a substantial part of lipid molecular species showed a significant correlation with insulin sensitivity indices we were able to identify a limited number of lipid metabolites of particular importance based on the LASSO approach. These few selected lipids with the closest
Sensitivity analysis in multi-parameter probabilistic systems

International Nuclear Information System (INIS)

Walker, J.R.

1987-01-01

Probabilistic methods involving the use of multi-parameter Monte Carlo analysis can be applied to a wide range of engineering systems. The output from the Monte Carlo analysis is a probabilistic estimate of the system consequence, which can vary spatially and temporally. Sensitivity analysis aims to examine how the output consequence is influenced by the input parameter values. Sensitivity analysis provides the necessary information so that the engineering properties of the system can be optimized. This report details a package of sensitivity analysis techniques that together form an integrated methodology for the sensitivity analysis of probabilistic systems. The techniques have known confidence limits and can be applied to a wide range of engineering problems. The sensitivity analysis methodology is illustrated by performing the sensitivity analysis of the MCROC rock microcracking model
Correlation between a single nucleotide polymorphism (G/T at nt ...

African Journals Online (AJOL)

Jane

2011-10-12

Oct 12, 2011 ... correlated to achieving sustained virological response (SVR) after pegylated interferon alpha and ribavirin ... ribavirin. Key words: Hepatitis C virus (HCV), interferon (IFN), myxovirus resistance protein (Mx1 protein), myxovirus .... long injection of interferon alpha 2b plus daily oral dose of ribavirin). If SVR is ...
Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Directory of Open Access Journals (Sweden)

Bin Xu

2017-01-01

Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.
Identification of groups with poor cost-effectiveness of peginterferon plus ribavirin for naïve hepatitis C patients with a real-world cohort and database.

Science.gov (United States)

Tsai, Pei-Chien; Liu, Ta-Wei; Tsai, Yi-Shan; Ko, Yu-Min; Chen, Kuan-Yu; Lin, Ching-Chih; Huang, Ching-I; Liang, Po-Cheng; Lin, Yi-Hung; Hsieh, Ming-Yen; Hou, Nai-Jen; Huang, Chung-Feng; Yeh, Ming-Lun; Lin, Zu-Yau; Chen, Shinn-Cherng; Dai, Chia-Yen; Chuang, Wan-Long; Huang, Jee-Fu; Yu, Ming-Lung

2017-06-01

For decades, peginterferon and ribavirin (PegIFN/RBV) have been the standard-of-care for chronic hepatitis C virus (CHC) infection. However, the actual cost-effectiveness of this therapy remains unclear. We purposed to explore the real-world cost effectiveness for subgroups of treatment-naïve CHC patients with PegIFN/RBV therapy in a large real-world cohort using a whole population database. A total of 1809 treatment-naïve chronic hepatitis C virus (HCV) patients (829 HCV genotype 1 [G1] and 980 HCV G2) treated with PegIFN/RBV therapies were linked to the National Health Insurance Research Database, covering the entire population of Taiwan from 1998 to 2013 to collect the total medical-care expenses of outpatient (antiviral agents, nonantiviral agents, laboratory, and consultation costs) and inpatient (medication, logistic, laboratory, and intervention costs) visits. The costs per treatment and the cost per sustained virological response (SVR) achieved were calculated. The average medical-care cost was USD $4823 (±$2984) per treatment and $6105 (±$3778) per SVR achieved. With SVR rates of 68.6% and 87.8%, the cost/SVR was significantly higher in G1 than those in G2 patients, respectively ($8285 vs $4663, P incurred significantly higher costs per SVR than their counterparts. The cost/SVR was extremely high among patients without RVR and in patients without cEVR. We investigated the real-world cost effectiveness data for different subgroups of treatment-naïve HCV patients with PegIFN/RBV therapies, which could provide useful, informative evidence for making decisions regarding future therapeutic strategies comprising costly direct-acting antivirals.
Slower Fibrosis Progression Among Liver Transplant Recipients With Sustained Virological Response After Hepatitis C Treatment

Science.gov (United States)

Habib, Shahid; Meister, Edward; Habib, Sana; Murakami, Traci; Walker, Courtney; Rana, Abbas; Shaikh, Obaid S.

2015-01-01

Background The natural course of hepatic fibrosis in HCV allograft recipients with sustained virological response (SVR) after anti-HCV therapy remains debatable. The aim of this study was to examine the progression of fibrosis in a cohort of patients who achieved SVR compared with those without treatment. Methods The 167 patients who met the inclusion and exclusion criteria were chosen from a transplant database. All patients were required to have histological evidence of recurrent HCV infection post-liver transplantation and a follow-up biopsy. The 140 of these patients had received anti-viral therapy. Twenty-seven patients were identified as controls and were matched with the treatment group in all respects. The patients were categorized into four groups based on treatment response: 1) no treatment (control) (n = 27); 2) non-responders (n = 81); 3) relapsers (n = 32); and 4) SVR (n = 27). The endpoint was the stage of fibrosis on the follow-up liver biopsy. Results The treated and untreated groups were similar in clinical characteristics at the time of transplantation and prior to the initiation of treatment. The 72% of the cohort showed a fibrosis progression of ≥ 1 stage; this change did not significantly differ between the patient groups. Nonetheless, the fibrosis progression rate was the highest in the untreated group and lowest in the patients who achieved SVR. A coefficient of determination was used. Improvements in fibrosis scores were found with greater treatment duration. These improvements were most evident with the achievement of SVR. Conclusions In conclusion, SVR after anti-viral therapy for recurrent hepatitis C infection post-transplantation was associated with slower fibrosis progression and significantly improved graft survival. PMID:27785303
Rapid virological response assessment by Abbott RealTime hepatitis C virus assay for predicting sustained virological responses in patients with hepatitis C virus genotype 1 treated with pegylated-interferon and ribavirin

Directory of Open Access Journals (Sweden)

Pei-yuan Su

2016-07-01

Full Text Available The lower limits of virus detection of hepatitis C virus (HCV RNA detection assays are continuously improving. We aimed to assess the utility of more precise definition of 4th week viral load [rapid virological response (RVR] in predicting sustained virological response (SVR in HCV genotype 1 patients treated with pegylated-interferon (PEG-IFN and ribavirin. Clinical data of treatment-naïve HCV genotype 1 patients were retrospectively collected from 2009 to 2014. Patients were grouped according to 4th week viral load as follows: undetectable (n = 90 and detectable but not quantifiable (< 12 IU/mL, n = 27. All patients received PEG-IFNα-2a or -2b and ribavirin for 24 weeks. Serum HCV RNA levels were measured by Abbott RealTime (ART; Abbott Molecular, Abbott Park, IL, USA HCV assay. SVR was 95.5% and 63% in the undetectable group and < 12 IU/mL group of 4th week viral load, respectively. The between-group difference in SVR was significant (p < 0.001. We determined 4th week viral load was independently associated with SVR (odds ratio = 19.28; p = 0.002 and a good predictor of SVR [area under the curve (AUC = 0.775; p = 0.001]. ART HCV assays had a stronger SVR predictive value in HCV genotype 1 patients, indicating that only the undetectable group of 4th week viral load patients measured by ART HCV assay should be considered for shorter treatment time (24 weeks with PEG-IFN and ribavirin.
Forecasts of non-Gaussian parameter spaces using Box-Cox transformations

Science.gov (United States)

Joachimi, B.; Taylor, A. N.

2011-09-01

Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter constraints by future weak gravitational lensing surveys. The characteristic non-linear degeneracy between matter density parameter and normalization of matter density fluctuations is reproduced for several cases, and the capabilities of breaking this degeneracy by weak-lensing three-point statistics is investigated. Possible applications of Box-Cox transformations of posterior distributions are discussed, including the prospects for performing statistical data analysis steps in the transformed Gaussianized parameter space.
Fault Detection for Non-Gaussian Stochastic Systems with Time-Varying Delay

Directory of Open Access Journals (Sweden)

Tao Li

2013-01-01

Full Text Available Fault detection (FD for non-Gaussian stochastic systems with time-varying delay is studied. The available information for the addressed problem is the input and the measured output probability density functions (PDFs of the system. In this framework, firstly, by constructing an augmented Lyapunov functional, which involves some slack variables and a tuning parameter, a delay-dependent condition for the existence of FD observer is derived in terms of linear matrix inequality (LMI and the fault can be detected through a threshold. Secondly, in order to improve the detection sensitivity performance, the optimal algorithm is applied to minimize the threshold value. Finally, paper-making process example is given to demonstrate the applicability of the proposed approach.
New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

Directory of Open Access Journals (Sweden)

YaJun Li

2015-01-01

Full Text Available The passivity problem for a class of stochastic neural networks systems (SNNs with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.
Zhang neural network for online solution of time-varying convex quadratic program subject to time-varying linear-equality constraints

International Nuclear Information System (INIS)

Zhang Yunong; Li Zhan

2009-01-01

In this Letter, by following Zhang et al.'s method, a recurrent neural network (termed as Zhang neural network, ZNN) is developed and analyzed for solving online the time-varying convex quadratic-programming problem subject to time-varying linear-equality constraints. Different from conventional gradient-based neural networks (GNN), such a ZNN model makes full use of the time-derivative information of time-varying coefficient. The resultant ZNN model is theoretically proved to have global exponential convergence to the time-varying theoretical optimal solution of the investigated time-varying convex quadratic program. Computer-simulation results further substantiate the effectiveness, efficiency and novelty of such ZNN model and method.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

Science.gov (United States)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.
Radon parameters in outdoor air

International Nuclear Information System (INIS)

Porstendoerfer, J.; Zock, Ch.; Wendt, J.; Reineking, A.

2002-01-01

For dose estimation by inhalation of the short lived radon progeny in outdoor air, the equilibrium factor (F), the unattached fraction (f p ), and the activity size distribution of the radon progeny were measured. Besides the radon parameter the meteorological parameter like temperature, wind speed, and rainfall intensity were registered. The measurements were carried out continuously for several weeks to find out the variation with time (day/night) and for different weather conditions. The radon gas, the unattached and aerosol-attached radon progenies were measured with an monitor developed for continuous measurements in outdoor air with low activity concentrations. For the determination of the activity size distribution a low pressure online alpha cascade impactor was used. The measured values of the equilibrium factor varied between 0.5-0.8 depending on weather conditions and time of the day. For high pressure weather conditions a diurnal variation of the F-factor was obtained. A lower average value (F=0.25) was registered during rainy days. The obtained f p -values varied between 0.04 and 0.12. They were higher than expected. The measured activity size distribution of the radon progeny averaged over a measurement period of three weeks can be approximated by a sum of three log-normal distributions. The greatest activity fraction is adsorbed on aerosol particles in the accumulation size range (100-1000 nm) with activity median diameters and geometric standard deviation values between 250-450 nm and 1.5-3.0, respectively. The activity median diameter of this accumulation mode in outdoor air was significantly greater than in indoor air (150-250 nm). An influence of the weather conditions on the activity of the accumulation particles was not significant. In contrast to the results of measurements in houses a small but significant fraction of the radon progeny (average value: 2%) is attached on coarse particles (>1000 nm). This fraction varied between 0-10%. 20
Role of deceleration parameter and interacting dark energy in singularity avoidance

Science.gov (United States)

Abdussattar; Prajapati, S. R.

2011-02-01

A class of non-singular bouncing FRW models are obtained by constraining the deceleration parameter in the presence of an interacting dark energy represented by a time-varying cosmological constant. The models being geometrically closed, initially accelerate for a certain period of time and decelerate thereafter and are also free from the entropy and cosmological constant problems. Taking a constant of integration equal to zero one particular model is discussed in some detail and the variation of different cosmological parameters are shown graphically for specific values of the parameters of the model. For some specific choice of the parameters of the model the ever expanding models of Ozer & Taha and Abdel-Rahman and the decelerating models of Berman and also the Einstein de-Sitter model may be obtained as special cases of this particular model.
Robust exponential stabilization of nonholonomic wheeled mobile robots with unknown visual parameters

Institute of Scientific and Technical Information of China (English)

无

2011-01-01

The visual servoing stabilization of nonholonomic mobile robot with unknown camera parameters is investigated.A new kind of uncertain chained model of nonholonomic kinemetic system is obtained based on the visual feedback and the standard chained form of type (1,2) mobile robot.Then,a novel time-varying feedback controller is proposed for exponentially stabilizing the position and orientation of the robot using visual feedback and switching strategy when the camera parameters are not known.The exponential s...
CO 2 laser cutting of MDF . 1. Determination of process parameter settings

Science.gov (United States)

Lum, K. C. P.; Ng, S. L.; Black, I.

2000-02-01

This paper details an investigation into the laser processing of medium-density fibreboard (MDF). Part 1 reports on the determination of process parameter settings for the effective cutting of MDF by CO 2 laser, using an established experimental methodology developed to study the interrelationship between and effects of varying laser set-up parameters. Results are presented for both continuous wave (CW) and pulse mode (PM) cutting, and the associated cut quality effects have been commented on.
Parameter and state estimation in nonlinear dynamical systems

Science.gov (United States)

Creveling, Daniel R.

This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling
Restoration of optimal ellipsoid left ventricular geometry: lessons learnt from in silico surgical modelling.

Science.gov (United States)

Adhyapak, Srilakshmi M; Menon, Prahlad G; Rao Parachuri, V

2014-02-01

Several issues that are inherent in the surgical techniques of surgical ventricular restoration (SVR) need specialized devices or techniques to overcome them, which may not always result in optimal outcomes. We used a non-invasive novel in silico modelling technique to study left ventricular (LV) morphology and function before and after SVR. The cardiac magnetic resonance imaging derived actual pre- and postoperative endocardial morphology and function was compared with the in silico analysis of the same. Cardiac magnetic resonance steady state free precession (SSFP) cine images were employed to segment endocardial surface contours over the cardiac cycle. Using the principle of Hausdorff distance to examine phase-to-phase regional endocardial displacement, dyskinetic/akinetic areas were identified at the instant of peak basal contraction velocity. Using a three-dimensional (3D) surface clipping tool, the maximally scarred, dyskinetic or akinetic LV antero-apical areas were virtually resected and a new apex was created. A virtual rectangular patch was created upon the clipped surface LV model by 3D Delaunay triangulation. Presurgical endocardial mechanical function quantified from cine cardiac magnetic resonance, using a technique of spherical harmonics (SPHARM) surface parameterization, was applied onto the virtually clipped and patched LV surface model. Finally, the in silico model of post-SVR LV shape was analysed for quantification of regional left ventricular volumes (RLVVs) and function. This was tested in 2 patients with post-myocardial infarction antero-apical LV aneuryms. Left ventricular mechanical dysynchrony was evaluated by RLVV analysis of pre-SVR, in silico post-SVR and actual post-SVR LV endocardial surface data. Following exclusion of the scarred areas, the virtual resected LV model demonstrated significantly lesser areas of akinesia. The decreases in regional LV volumes in the in silico modelling were significant and comparable with the actual
Daily air quality index forecasting with hybrid models: A case in China

International Nuclear Information System (INIS)

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-01-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the
Implementing and scaling up HCV treatment services for people who inject drugs and other high risk groups in Ukraine: An evaluation of programmatic and treatment outcomes.

Science.gov (United States)

Mazhnaya, Alyona; Meteliuk, Anna; Barnard, Tetiana; Zelenev, Alexei; Filippovych, Sergii; Altice, Frederick L

2017-09-01

HCV prevalence estimates among people who inject drugs (PWID) in Ukraine is high (60-90%), yet barriers to HCV treatment and care remain substantial including limited access to direct acting antiviral (DAA) medications. A feasibility scale-up project implemented HCV treatment in community-based settings to improve access to DAA treatment for key populations in this context. Using program-level data and verified medical records, we describe the development, implementation processes and outcomes for HCV treatment for PWID and other risks groups. Most participants (76%) received a combination of sofosbuvir, pegylated interferon, and ribavirin for 12 weeks. Treatment enrollment started in June 2015; the first two waves are reported. Data on demographics, HIV characteristics, HCV genotype and RNA levels, including sustained virologic response (SVR) were obtained from verified medical records. We used logistic regression to examine the independent correlates of achieving a SVR. The project was implemented in 19 healthcare institutions from 16 regions of Ukraine, mainly within AIDS specialty centers. Our analytical sample included 1126 participants who were mostly men (73%) and the majority were HIV co-infected (79%). Treatment retention was 97.7%; the proportions of participants who achieved SVR for the overall sample and for those with complete data (N=1029) were 86.2% (95% CI 84.08-88.19%) and 94.3% (95% CI 92.8-95.7%) respectively. The analysis of data restricted to only those with SVR data available showed that PWID who were currently injecting had comparable SVR rates (89.2%, 95% CI 81.5-94.5%) to PWID not injecting (94.4%, 95% CI 92.4-96.1), PWID on methadone (94.4%, 95%CI 92.4-96.1), and 'other' risk groups (95.2%, 95% CI 91.3-97.7). Independent factors associated with achieving a SVR were female sex (AOR: 3.44, 95% CI 1.45-8.14), HCV genotype 3 (AOR: 4.57, 95% CI 1.97-10.59) compared to genotype 1. SVR rates in PWID actively injecting did not differ significantly
Prediction of monomer reactivity in radical copolymerizations from transition state quantum chemical descriptors

Directory of Open Access Journals (Sweden)

Zhengde Tan

2013-01-01

Full Text Available In comparison with the Q-e scheme, the Revised Patterns Scheme: the U, V Version (the U-V scheme has greatly improved both its accessibility and its accuracy in interpreting and predicting the reactivity of a monomer in free-radical copolymerizations. Quantitative structure-activity relationship (QSAR models were developed to predict the reactivity parameters u and v of the U-V scheme, by applying genetic algorithm (GA and support vector machine (SVM techniques. Quantum chemical descriptors used for QSAR models were calculated from transition state species with structures C¹H3 - C²HR³• or •C¹H2 - C²H2R³ (formed from vinyl monomers C¹H²=C²HR³ + H•, using density functional theory (DFT, at the UB3LYP level of theory with 6-31G(d basis set. The optimum support vector regression (SVR model of the reactivity parameter u based on Gaussian radial basis function (RBF kernel (C = 10, ε = 10- 5 and γ = 1.0 produced root-mean-square (rms errors for the training, validation and prediction sets being 0.220, 0.326 and 0.345, respectively. The optimal SVR model for v with the RBF kernel (C = 20, ε = 10- 4 and γ = 1.2 produced rms errors for the training set of 0.123, the validation set of 0.206 and the prediction set of 0.238. The feasibility of applying the transition state quantum chemical descriptors to develop SVM models for reactivity parameters u and v in the U-V scheme has been demonstrated.
English Word-Level Decoding and Oral Language Factors as Predictors of Third and Fifth Grade English Language Learners' Reading Comprehension Performance

Science.gov (United States)

Landon, Laura L.

2017-01-01

This study examines the application of the Simple View of Reading (SVR), a reading comprehension theory focusing on word recognition and linguistic comprehension, to English Language Learners' (ELLs') English reading development. This study examines the concurrent and predictive validity of two components of the SVR, oral language and word-level…
Slowly varying dilaton cosmologies and their field theory duals

International Nuclear Information System (INIS)

Awad, Adel; Das, Sumit R.; Ghosh, Archisman; Oh, Jae-Hyuk; Trivedi, Sandip P.

2009-01-01

We consider a deformation of the AdS 5 xS 5 solution of IIB supergravity obtained by taking the boundary value of the dilaton to be time dependent. The time dependence is taken to be slowly varying on the anti-de Sitter (AdS) scale thereby introducing a small parameter ε. The boundary dilaton has a profile which asymptotes to a constant in the far past and future and attains a minimum value at intermediate times. We construct the supergravity (sugra) solution to first nontrivial order in ε, and find that it is smooth, horizon-free, and asymptotically AdS 5 xS 5 in the far future. When the intermediate values of the dilaton becomes small enough the curvature becomes of order the string scale and the sugra approximation breaks down. The resulting dynamics is analyzed in the dual SU(N) gauge theory on S 3 with a time dependent coupling constant which varies slowly. When Nε 5 xS 5 again. When Nε>>1, we formulate a classical adiabatic perturbation theory based on coherent states which arises in the large N limit. For large values of the 't Hooft coupling this reproduces the supergravity results. For small 't Hooft coupling the coherent state calculations become involved and we cannot reach a definite conclusion. We argue that the final state should have a dual description which is mostly smooth AdS 5 space with the possible presence of a small black hole.
Validation of the Simple View of Reading in Hebrew--A Semitic Language

Science.gov (United States)

Joshi, R. Malatesha; Ji, Xuejun Ryan; Breznitz, Zvia; Amiel, Meirav; Yulia, Astri

2015-01-01

The Simple View of Reading (SVR) in Hebrew was tested by administering decoding, listening comprehension, and reading comprehension measures to 1,002 students from Grades 2 to 10 in the northern part of Israel. Results from hierarchical regression analyses supported the SVR in Hebrew with decoding and listening comprehension measures explaining…
Toward the integration of European natural gas markets:A time-varying approach

International Nuclear Information System (INIS)

Renou-Maissant, Patricia

2012-01-01

Over the past fifteen years, European gas markets have radically changed. In order to build a single European gas market, a new regulatory framework has been established through three European Gas Directives. The purpose of this article is to investigate the impact of the reforms in the natural gas industry on consumer prices, with a specific focus on gas prices for industrial use. The strength of the relationship between the industrial gas prices of six western European countries is studied by testing the Law of One Price for the period 1991–2009. Estimations were carried out using both cointegration analysis and time-varying parameter models. Results highlight an emerging and on-going process of convergence between the industrial gas prices in western Europe since 2001 for the six EU member states. The strength and the level of convergence differ widely between countries. Strong integration of gas markets in continental Europe, except for the Belgian market, has been established. It appears that the convergence process between continental countries and the UK is not completed. Thus, the integration of European gas markets remains an open issue and the question of how far integration will proceed will still be widely discussed in the coming years. - Highlights: ► We investigate the integration of European natural gas markets. ► We use both cointegration analysis and time-varying parameter models. ► We show the failure of cointegration techniques to take account of evolving processes. ► An emerging and on-going process of convergence between the industrial gas prices is at work. ► Strong integration of gas markets in continental Europe has been established.
Spacetime-varying couplings and Lorentz violation

International Nuclear Information System (INIS)

Kostelecky, V. Alan; Lehnert, Ralf; Perry, Malcolm J.

2003-01-01

Spacetime-varying coupling constants can be associated with violations of local Lorentz invariance and CPT symmetry. An analytical supergravity cosmology with a time-varying fine-structure constant provides an explicit example. Estimates are made for some experimental constraints
Performance parameters of a standalone PV plant

International Nuclear Information System (INIS)

El Fathi, Amine; Nkhaili, Lahcen; Bennouna, Amin; Outzourhit, Abdelkader

2014-01-01

Highlights: • We described in details a photovoltaic power plant installed in the remote rural village Elkaria (Essaouira Morocco – 7.2 kWp). • We presented the results of monitoring and some performance parameters of the plant such as load curve. • We discussed the energy management of the plant which is based on the droop mode control. • We presented and discussed the yields and the performance ratio of the plant. - Abstract: In this work we present a detailed description of a 7.2 kWp photovoltaic power plant installed in the remote rural village Elkaria (province of Essaouira in Morocco). This plant supplies 16 households with electricity through a local grid that was installed for this purpose. The results of monitoring some performance parameters of the plant such as load curve, the yields and the performance ratio are presented and discussed. The performance ratio of the PV plant varied between 33% and 70.2%. The low values of this parameter are mainly attributed to the way the battery inverter manages the energy flow
Identification of a Discontinuous Parameter in Stochastic Parabolic Systems

International Nuclear Information System (INIS)

Aihara, S. I.

1998-01-01

The purpose of this paper is to study the identification problem for a spatially varying discontinuous parameter in stochastic diffusion equations. The consistency property of the maximum likelihood estimate (M.L.E.) and a generating algorithm for M.L.E. have been explored under the condition that the unknown parameter is in a sufficiently regular space with respect to spatial variables. In order to prove the consistency property of the M.L.E. for a discontinuous diffusion coefficient, we use the method of sieves, i.e., first the admissible class of unknown parameters is projected into a finite-dimensional space and next the convergence of the derived finite-dimensional M.L.E. to the infinite-dimensional M.L.E. is justified under some conditions. An iterative algorithm for generating the M.L.E. is also proposed with two numerical examples
Effects of reaction-kinetic parameters on modeling reaction pathways in GaN MOVPE growth

Science.gov (United States)

Zhang, Hong; Zuo, Ran; Zhang, Guoyi

2017-11-01

In the modeling of the reaction-transport process in GaN MOVPE growth, the selections of kinetic parameters (activation energy Ea and pre-exponential factor A) for gas reactions are quite uncertain, which cause uncertainties in both gas reaction path and growth rate. In this study, numerical modeling of the reaction-transport process for GaN MOVPE growth in a vertical rotating disk reactor is conducted with varying kinetic parameters for main reaction paths. By comparisons of the molar concentrations of major Ga-containing species and the growth rates, the effects of kinetic parameters on gas reaction paths are determined. The results show that, depending on the values of the kinetic parameters, the gas reaction path may be dominated either by adduct/amide formation path, or by TMG pyrolysis path, or by both. Although the reaction path varies with different kinetic parameters, the predicted growth rates change only slightly because the total transport rate of Ga-containing species to the substrate changes slightly with reaction paths. This explains why previous authors using different chemical models predicted growth rates close to the experiment values. By varying the pre-exponential factor for the amide trimerization, it is found that the more trimers are formed, the lower the growth rates are than the experimental value, which indicates that trimers are poor growth precursors, because of thermal diffusion effect caused by high temperature gradient. The effective order for the contribution of major species to growth rate is found as: pyrolysis species > amides > trimers. The study also shows that radical reactions have little effect on gas reaction path because of the generation and depletion of H radicals in the chain reactions when NH2 is considered as the end species.
Loading factor and inclination parameter of diagonal type MHD generators

International Nuclear Information System (INIS)

Ishikawa, Motoo

1979-01-01

Regarding diagonal type MHD generators is studied the relation between the loading factor and inclination parameter which is required for attaining the maximum power density with a given electrical efficiency on the assumption of infinitely segmented electrodes. The average current density on electrodes is calculated against the Hall parameter, loading factor, and inclination parameter. The diagonal type generator is compared with Faraday type generator regarding the average current density. Decreasing the loading factor from inlet to outlet is appropriate to small size generators but increasing to large size generators. The inclination parameter had better decrease in both generators, being smaller for small generators than for large ones. The average current density on electrodes of diagonal type generators varies less with the loading factor than the Faraday type. In large size generators its value can become smaller compared with that of the Faraday type. (author)
Can we predict uranium bioavailability based on soil parameters? Part 1: Effect of soil parameters on soil solution uranium concentration

International Nuclear Information System (INIS)

Vandenhove, H.; Hees, M. van; Wouters, K.; Wannijn, J.

2007-01-01

Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for 238 U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K d , L kg -1 ) and the organic matter content (R 2 = 0.70) and amorphous Fe content (R 2 = 0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH = 6, log(K d ) was linearly related with pH [log(K d ) = - 1.18 pH + 10.8, R 2 = 0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex. - Uranium solubility in soil can be predicted from organic matter or amorphous iron content and pH or with complex multilinear models considering several soil parameters

Statistical classifiers on multifractal parameters for optical diagnosis of cervical cancer

Science.gov (United States)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Kumar, Rajeev; Krishnamoorthy, Vigneshram; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-06-01

An augmented set of multifractal parameters with physical interpretations have been proposed to quantify the varying distribution and shape of the multifractal spectrum. The statistical classifier with accuracy of 84.17% validates the adequacy of multi-feature MFDFA characterization of elastic scattering spectroscopy for optical diagnosis of cancer.
On the motion of non-linear oscillators with a fractional-order restoring force and time variable parameters

International Nuclear Information System (INIS)

Kovacic, Ivana

2009-01-01

An analytical approach to determine the approximate solution for the periodic motion of non-conservative oscillators with a fractional-order restoring force and slowly varying parameters is presented. The solution has the form of the first-order differential equation for the amplitude and phase of motion. The method used is based on the combination of the Krylov-Bogoliubov method with Hamilton's variational principle with the uncommutative rule for the variation of velocity. The conservative systems with slowly varying parameters are also considered. The corresponding adiabatic invariant is obtained. Two examples are given to illustrate derived theoretical results.
Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.
Angiotensin II and CRF receptors in the central nucleus of the amygdala mediate hemodynamic response variability to cocaine in conscious rats.

Science.gov (United States)

Watanabe, Mari A; Kucenas, Sarah; Bowman, Tamara A; Ruhlman, Melissa; Knuepfer, Mark M

2010-01-14

Stress or cocaine evokes either a large increase in systemic vascular resistance (SVR) or a smaller increase in SVR accompanied by an increase in cardiac output (designated vascular and mixed responders, respectively) in Sprague-Dawley rats. We hypothesized that the central nucleus of the amygdala (CeA) mediates this variability. Conscious, freely-moving rats, instrumented for measurement of arterial pressure and cardiac output and for drug delivery into the CeA, were given cocaine (5 mg/kg, iv, 4-6 times) and characterized as vascular (n=15) or mixed responders (n=10). Subsequently, we administered cocaine after bilateral microinjections (100 nl) of saline or selective agents in the CeA. Muscimol (80 pmol), a GABA(A) agonist, or losartan (43.4 pmol), an AT(1) receptor antagonist, attenuated the cocaine-induced increase in SVR in vascular responders, selectively, such that vascular responders were no longer different from mixed responders. The corticotropin releasing factor (CRF) antagonist, alpha-helical CRF(9-41) (15.7 pmol), abolished the difference between cardiac output and SVR in mixed and vascular responders. We conclude that greater increases in SVR observed in vascular responders are dependent on AT(1) receptor activation and, to a lesser extent on CRF receptors. Therefore, AT(1) and CRF receptors in the CeA contribute to hemodynamic response variability to intravenous cocaine.
The hemodynamic effects of methylene blue when administered at the onset of cardiopulmonary bypass.

Science.gov (United States)

Maslow, Andrew D; Stearns, Gary; Butala, Parag; Batula, Parag; Schwartz, Carl S; Gough, Jeffrey; Singh, Arun K

2006-07-01

Hypotension occurs during cardiopulmonary bypass (CPB), in part because of induction of the inflammatory response, for which nitric oxide and guanylate cyclase play a central role. In this study we examined the hemodynamic effects of methylene blue (MB), an inhibitor of guanylate cyclase, administered during cardiopulmonary bypass (CPB) to patients taking angiotensin-converting enzyme inhibitors. Thirty patients undergoing cardiac surgery were randomized to receive either MB (3 mg/kg) or saline (S) after institution of CPB and cardioplegic arrest. CPB was managed similarly for all study patients. Hemodynamic data were assessed before, during, and after CPB. The use of vasopressors was recorded. All study patients experienced a similar reduction in mean arterial blood pressure (MAP) and systemic vascular resistance (SVR) with the onset of CPB and cardioplegic arrest. MB increased MAP and SVR and this effect lasted for 40 minutes. The saline group demonstrated a persistently reduced MAP and SVR throughout CPB. The saline group received phenylephrine more frequently during CPB, and more norepinephrine after CPB to maintain a desirable MAP. The MB group recorded significantly lower serum lactate levels despite equal or greater MAP and SVR. In conclusion, administration of MB after institution of CPB for patients taking angiotensin-converting enzyme inhibitors increased MAP and SVR and reduced the need for vasopressors. Furthermore, serum lactate levels were lower in MB patients, suggesting more favorable tissue perfusion.
Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

International Nuclear Information System (INIS)

Xu Ruirui; Chen Tianlun; Gao Chengfeng

2006-01-01

Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
Kinematic hand parameters in front crawl at different paces of swimming.

Science.gov (United States)

Samson, Mathias; Monnet, Tony; Bernard, Anthony; Lacouture, Patrick; David, Laurent

2015-11-05

The aim of this study was to investigate the evolution of kinematic hand parameters (sweepback angle, angle of attack, velocity, acceleration and orientation of the hand relative to the absolute coordinate system) throughout an aquatic stroke and to study the possible modifications caused by a variation of the swimming pace. Seventeen competitive swimmers swam at long distance, middle distance and sprint paces. Parameters were calculated from the trajectory of seven markers on the hand measured with an optoelectronic system. Results showed that kinematic hand parameters evolve differently depending on the pace. Angle of attack, sweepback angle, acceleration and orientation of the hand do not vary significantly. The velocity of the hand increases when the pace increases, but only during the less propulsive phases (entry and stretch and downsweep to catch). The more the pace increases and the more the absolute durations of the entry and stretch and downsweep to catch phases decrease. Absolute durations of the insweep and upsweep phases remain constant. During these phases, the propulsive hand forces calculated do not vary significantly when the pace increases. The increase of swimming pace is then explained by the swimmer's capacity to maintain propulsive phases rather than increasing the force generation within each cycle. Copyright © 2015 Elsevier Ltd. All rights reserved.
Effects of varying the longitudinal dispersion and no drip cask rate failures upon Yucca Mountain site performance

International Nuclear Information System (INIS)

Winterle, Bret

2001-01-01

Proposed changes in the regulatory time limits used for viability assessments of the proposed national high-level radioactive waste repository in Yucca Mountain, Nevada from 10,000 years to 100,000 or even 1,000,000 years call into question both the modelling techniques used to represent the repository's long-term performance, and our ability to extrapolate technological, climatological and geological phenomenon. Using a high-powered risk-assessment software program called Goldsim that a simplified total system performance assessment (STSPA) was designed for, the effects of varying the performance parameters of two barrier systems, one natural and one man-made, upon the total system performance were observed. The conclusion reached by varying these two parameters is that for a regulatory guideline of 10,000 years, there is no noticeable effect on the total system performance, but at 300,000 years, it appears that the effect of reducing the longitudinal dispersion rate (a natural barrier) by one order of magnitude produced an astronomically high receptor dose, indicating that as predicted, our abilities to model situations beyond our ability to accurately extrapolate current scientific research is futile. (author)
Effects of varying the longitudinal dispersion and no drip cask rate failures upon Yucca Mountain site performance

Energy Technology Data Exchange (ETDEWEB)

Winterle, Bret

2001-07-01

Proposed changes in the regulatory time limits used for viability assessments of the proposed national high-level radioactive waste repository in Yucca Mountain, Nevada from 10,000 years to 100,000 or even 1,000,000 years call into question both the modelling techniques used to represent the repository's long-term performance, and our ability to extrapolate technological, climatological and geological phenomenon. Using a high-powered risk-assessment software program called Goldsim that a simplified total system performance assessment (STSPA) was designed for, the effects of varying the performance parameters of two barrier systems, one natural and one man-made, upon the total system performance were observed. The conclusion reached by varying these two parameters is that for a regulatory guideline of 10,000 years, there is no noticeable effect on the total system performance, but at 300,000 years, it appears that the effect of reducing the longitudinal dispersion rate (a natural barrier) by one order of magnitude produced an astronomically high receptor dose, indicating that as predicted, our abilities to model situations beyond our ability to accurately extrapolate current scientific research is futile. (author)
Sensitivity of ring growth and carbon allocation to climatic variation vary within ponderosa pine trees.

Science.gov (United States)

Kerhoulas, Lucy P; Kane, Jeffrey M

2012-01-01

Most dendrochronological studies focus on cores sampled from standard positions (main stem, breast height), yet vertical gradients in hydraulic constraints and priorities for carbon allocation may contribute to different growth sensitivities with position. Using cores taken from five positions (coarse roots, breast height, base of live crown, mid-crown branch and treetop), we investigated how radial growth sensitivity to climate over the period of 1895-2008 varies by position within 36 large ponderosa pines (Pinus ponderosa Dougl.) in northern Arizona. The climate parameters investigated were Palmer Drought Severity Index, water year and monsoon precipitation, maximum annual temperature, minimum annual temperature and average annual temperature. For each study tree, we generated Pearson correlation coefficients between ring width indices from each position and six climate parameters. We also investigated whether the number of missing rings differed among positions and bole heights. We found that tree density did not significantly influence climatic sensitivity to any of the climate parameters investigated at any of the sample positions. Results from three types of analyses suggest that climatic sensitivity of tree growth varied with position height: (i) correlations of radial growth and climate variables consistently increased with height; (ii) model strength based on Akaike's information criterion increased with height, where treetop growth consistently had the highest sensitivity and coarse roots the lowest sensitivity to each climatic parameter; and (iii) the correlation between bole ring width indices decreased with distance between positions. We speculate that increased sensitivity to climate at higher positions is related to hydraulic limitation because higher positions experience greater xylem tensions due to gravitational effects that render these positions more sensitive to climatic stresses. The low sensitivity of root growth to all climatic variables
Adaptive operational modal identification for slow linear time-varying structures based on frozen-in coefficient method and limited memory recursive principal component analysis

Science.gov (United States)

Wang, Cheng; Guan, Wei; Wang, J. Y.; Zhong, Bineng; Lai, Xiongming; Chen, Yewang; Xiang, Liang

2018-02-01

To adaptively identify the transient modal parameters for linear weakly damped structures with slow time-varying characteristics under unmeasured stationary random ambient loads, this paper proposes a novel operational modal analysis (OMA) method based on the frozen-in coefficient method and limited memory recursive principal component analysis (LMRPCA). In the modal coordinate, the random vibration response signals of mechanical weakly damped structures can be decomposed into the inner product of modal shapes and modal responses, from which the natural frequencies and damping ratios can be well acquired by single-degree-of-freedom (SDOF) identification approach such as FFT. Hence, for the OMA method based on principal component analysis (PCA), it becomes very crucial to examine the relation between the transformational matrix and the modal shapes matrix, to find the association between the principal components (PCs) matrix and the modal responses matrix, and to turn the operational modal parameter identification problem into PCA of the stationary random vibration response signals of weakly damped mechanical structures. Based on the theory of "time-freezing", the method of frozen-in coefficient, and the assumption of "short time invariant" and "quasistationary", the non-stationary random response signals of the weakly damped and slow linear time-varying structures (LTV) can approximately be seen as the stationary random response time series of weakly damped and linear time invariant structures (LTI) in a short interval. Thus, the adaptive identification of time-varying operational modal parameters is turned into decompositing the PCs of stationary random vibration response signals subsection of weakly damped mechanical structures after choosing an appropriate limited memory window. Finally, a three-degree-of-freedom (DOF) structure with weakly damped and slow time-varying mass is presented to illustrate this method of identification. Results show that the LMRPCA
Analysis of Anderson-Grueneisen parameter under high temperature in alkaline earthoxides

International Nuclear Information System (INIS)

Pandey, Vipra; Gupta, Seema; Tomar, D.S.; Goyal, S.C.

2010-01-01

The Anderson-Grueneisen parameter (δ) is of considerable importance to Earth scientists because it sets limitations on the thermo-elastic properties of the lower mantle and core. However, there are several formulations on the Grueneisen parameter, which are in frequent use and predict varying dependence of δ as a function of temperature. In this paper, the expressions for thermal expansion, thermal expansion coefficients and bulk modulus are obtained considering the anharmonic dependence on temperature and are applied to study these constants to alkaline earth oxides. Using the derived expressions, we have shown that different parameters on which the Anderson-Grueneisen parameter (δ) depends are temperature dependent, but above all the Anderson-Grueneisen parameter (δ) is independent of temperature. The results obtained have been found to be comparable to experimental data. -- Research Highlights: → The Anderson-Grueneisen parameter (δ) is independent of temperature. → Three parameters, volume coefficient of thermal expansion, bulk modulus, and the Anderson-Grueneisen parameter, can completely describe the thermo-physical behavior of a solid. → Useful in analyzing the thermo-elastic behavior, microscopic behavior, internal structure and other related properties of AEO.
Dual ant colony operational modal analysis parameter estimation method

Science.gov (United States)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
Generation of a library of two-group diffusion and kinetics parameters for DYN3D

International Nuclear Information System (INIS)

Petkov, P.T.; Christoskov, I.D.; Kamenov, K.; Antov, A.

2002-01-01

A library of two-group diffusion and kinetics parameters has been generated for the neutron kinetics code DYN3D for analysis of reactivity initiated accidents for the WWER-440 reactors, based on the MAGRU approximation methodology for the diffusion and kinetics parameters. The accuracy of this methodology has been tested and the conclusion is that it is not adequate. A new approximation methodology, based on interpolation for the most widely varying parameters, i.e. the moderator temperature and density, and on approximation for all other independent parameters, is presented. The methodology of calculation of the kinetics parameters using primary data from ENDF-B/VI is described in detail (Authors)
Robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays

International Nuclear Information System (INIS)

Balasubramaniam, P.; Lakshmanan, S.; Manivannan, A.

2012-01-01

Highlights: ► Robust stability analysis for Markovian jumping interval neural networks is considered. ► Both linear fractional and interval uncertainties are considered. ► A new LKF is constructed with triple integral terms. ► MATLAB LMI control toolbox is used to validate theoretical results. ► Numerical examples are given to illustrate the effectiveness of the proposed method. - Abstract: This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.
The influence of various dielectric parameters on the reststrahlen region of SiC

International Nuclear Information System (INIS)

Engelbrecht, J.A.A.; Rooyen, I.J. van

2011-01-01

The reststrahlen region of SiC is analysed with the goal of establishing the origin of different shapes of this band, by varying the dielectric parameters involved when simulating the reststrahlen region as obtained by infrared reflectance. -- Research highlights: → An anomalous peak observed in the reststrahlen band of SiC was investigated. → The reflection spectrum of SiC in the reststrahlen region was simulated by theoretical calculations. → The influence on the reststrahlen band of the dielectric parameters used in the simulations is discussed. → Dielectric parameters used in the simulations did not yield the anomalous peak that is observed experimentally.
The influence of various dielectric parameters on the reststrahlen region of SiC

Energy Technology Data Exchange (ETDEWEB)

Engelbrecht, J.A.A., E-mail: Japie.Engelbrecht@nmmu.ac.z [Physics Department, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth 6031 (South Africa); Rooyen, I.J. van [Physics Department, Nelson Mandela Metropolitan University, PO Box 77000, Port Elizabeth 6031 (South Africa); National Laser Centre, CSIR, PO Box 395, Pretoria 0001 (South Africa)

2011-02-01

The reststrahlen region of SiC is analysed with the goal of establishing the origin of different shapes of this band, by varying the dielectric parameters involved when simulating the reststrahlen region as obtained by infrared reflectance. -- Research highlights: {yields} An anomalous peak observed in the reststrahlen band of SiC was investigated. {yields} The reflection spectrum of SiC in the reststrahlen region was simulated by theoretical calculations. {yields} The influence on the reststrahlen band of the dielectric parameters used in the simulations is discussed. {yields} Dielectric parameters used in the simulations did not yield the anomalous peak that is observed experimentally.
Ion source plasma parameters measurement based on Langmuir probe with commercial frequency sweep

International Nuclear Information System (INIS)

Xie, Y.H.; Hu, C.D.; Liu, S.; Shong, S.H.; Jiang, C.C.; Liu, Z.M.

2010-01-01

Langmuir probe is one of the main diagnostic tools to measure the plasma parameters in the ion source. In this article, the commercial frequency power, which is sine wave of 50 Hz, was supplied on the Langmuir probe to measure the plasma parameters. The best feature of this probe sweep voltage is that it does not need extra design. The probe I-V characteristic curve can be got in less than 5 ms and the plasma parameters, the electron temperature and the electron density, varying with the time can be got in one plasma discharge of 400 ms.
PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient

Directory of Open Access Journals (Sweden)

Alrijadjis .

2014-12-01

Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.
Diabetes and Cirrhosis Are Risk Factors for Hepatocellular Carcinoma After Successful Treatment of Chronic Hepatitis C.

Science.gov (United States)

Hedenstierna, Magnus; Nangarhari, Ali; Weiland, Ola; Aleman, Soo

2016-09-15

Successful treatment of hepatitis C virus (HCV) infection reduces the risk for hepatocellular carcinoma (HCC), but a risk remains. Current guidelines recommend continued HCC surveillance after sustained virologic response (SVR) has been achieved. This study aimed to investigate risk factors and incidence rates for HCC after SVR in HCV patients with pretreatment advanced liver disease (Metavir stage F3/F4). All patients with advanced liver disease successfully treated for HCV at Karolinska University Hospital during 1992-2013 (n = 399) were followed up for a median of 7.8 years. Data from national registries were used to minimize loss to follow-up. Incidence rates and hazard ratios (HRs) for development of HCC were calculated by Cox regression analysis. Seventeen patients developed HCC during 3366 person-years (PY) of follow-up. The HCC incidence rate was 0.95 (95% confidence interval [CI], .57-1.6) and 0.15 (95% CI, .05-.49) per 100 PY for patients with pretreatment F4 and F3, respectively. Patients with pretreatment cirrhosis and diabetes had a HR to develop HCC of 6.3, and an incidence rate of 7.9 per 100 PY (95% CI, 3.3-19) during the first 2 years of follow-up. The risk for HCC decreased significantly 2 years after SVR had been achieved. Diabetes mellitus and cirrhosis are strong risk factors for HCC development after SVR has been achieved. The risk to develop HCC diminishes significantly 2 years after SVR. Patients without cirrhosis have a low risk to develop HCC after SVR, and the benefit of HCC surveillance for this group is questionable. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Sofosbuvir and daclatasvir therapy in patients with hepatitis C-related advanced decompensated liver disease (MELD ≥ 15).

Science.gov (United States)

McCaughan, G W; Thwaites, P A; Roberts, S K; Strasser, S I; Mitchell, J; Morales, B; Mason, S; Gow, P; Wigg, A; Tallis, C; Jeffrey, G; George, J; Thompson, A J; Parker, F C; Angus, P W

2018-02-01

Antiviral therapy for hepatitis C has the potential to improve liver function in patients with decompensated cirrhosis. To examine the virological response and effect of viral clearance in patients with decompensated hepatitis C cirrhosis all with MELD scores ≥15 following sofosbuvir/daclatasvir ± ribavirin. We prospectively collected data on patients who commenced sofosbuvir/daclatasvir for 24-weeks under the Australian patient supply program (TOSCAR) and analysed outcomes including sustained viral response at 12 weeks (SVR12), death and transplant. 108 patients (M/F, 79/29; median age 56years; Child-Pugh 10; MELD 16; genotype 1/3, 55/47) received sofosbuvir/daclatasvir and two also received ribavirin. On intention-to-treat, the SVR12 rate was 70% (76/108). Seventy-eight patients completed 24-weeks therapy. SVR12 was achieved in 56 of these patients on per-protocol-analysis (76%). SVR12 was 80% in genotype 1 compared to 69% in genotype 3. Thirty patients failed to complete therapy. In patients achieving SVR12, median MELD and Child-Pugh fell from 16(IQR15-17) to 14(12-17) and 10(9-11) to 8(7-9), respectively (P<.001). In those who died, MELD increased from 16 to 23 at death (P=.036). Patients who required transplantation had a significantly higher baseline MELD (20) compared to those patients completing treatment (16) (P=.0010). The odds ratio for transplant in patients with baseline MELD ≥20 was 13.8(95%CI 2.78-69.04). SVR12 rates with sofosbuvir/daclatasvir in advanced liver disease are lower than in compensated disease. Although treatment improves MELD and Child-Pugh in most patients, a significant proportion will die or require transplantation. In those with MELD ≥20, it may be better to delay treatment until post-transplant. © 2017 John Wiley & Sons Ltd.
Real-World Study on Sofosbuvir-based Therapies in Asian Americans With Chronic Hepatitis C.

Science.gov (United States)

Pan, Calvin Q; Tiongson, Benjamin C; Hu, Ke-Qin; Han, Steven-Huy B; Tong, Myron; Chu, Danny; Park, James; Lee, Tai Ping; Bhamidimarri, Kalyan Ram; Ma, Xiaoli; Xiao, Pei Ying; Mohanty, Smruti R; Wang, Dan

2018-06-16

Limited data exist with regard to treatment outcomes in Asian Americans with chronic hepatitis C (CHC). We evaluated sofosbuvir (SOF)-based regimens in a national cohort of Asian Americans. Eligible Asian Americans patients with CHC who had posttreatment follow-up of 24 weeks for SOF -based therapies from December 2013 to June 2017 were enrolled from 11 sites across the United States. The primary endpoint was sustained virologic response (SVR) rates at posttreatment weeks 12 and 24. Secondary endpoints were to evaluate safety by tolerability and adverse events (AEs). Among 231 patients screened, 186 were enrolled. At baseline, 31% (57/186) patients were cirrhotic, 34% (63/186) were treatment experienced. Most of the subjects (42%, 79/186) received ledispavir/SOF therapy. The overall SVR12 was 95%, ranging from 86% in genotype (GT) 1b on SOF+ribavirin to 100% in GT 1b patients on ledipasvir/SOF at subgroup analyses. SVR12 was significantly lower in cirrhotic than in noncirrhotic patients [88% (50/57) vs. 98% (126/129), P<0.01]. Stratified by GT, SVR12 were: 96% (43/45) in GT 1a; 93% (67/72) in GT 1b; 100% (23/23) in GT 2; 90% (19/21) in GT 3; 100% (1/1) in GT 4; 83% (5/6) in GT 5; and 100% (16/16) in GT 6. Cirrhotic patients with treatment failure were primarily GT 1, (GT 1a, n=2; GT 1b, n=4) with 1 GT 5 (n=1). Patients tolerated the treatment without serious AEs. Late relapse occurred in 1 patient after achieving SVR12. In Asian Americans with CHC, SOF-based regimens were well tolerated without serious AEs and could achieve high SVR12 regardless of hepatitis C viral infection GT.
Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

Science.gov (United States)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.
Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

International Nuclear Information System (INIS)

Vadivel, P; Sakthivel, R; Mathiyalagan, K; Arunkumar, A

2013-01-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov–Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results. (paper)
Parameters Design for Logarithmic Quantizer Based on Zoom Strategy

Directory of Open Access Journals (Sweden)

Jingjing Yan

2017-01-01

Full Text Available This paper is concerned with the problem of designing suitable parameters for logarithmic quantizer such that the closed-loop system is asymptotic convergent. Based on zoom strategy, we propose two methods for quantizer parameters design, under which it ensures that the state of the closed-loop system can load in the invariant sets after some certain moments. Then we obtain that the quantizer is unsaturated, and thus the quantization errors are bounded under the time-varying logarithm quantization strategy. On that basis, we obtain that the closed-loop system is asymptotic convergent. A benchmark example is given to show the usefulness of the proposed methods, and the comparison results are illustrated.
Influences of image resolution on herbaceous root morphological parameters

Directory of Open Access Journals (Sweden)

ZHANG Zeyou

2014-06-01

Full Text Available Root images of four herbaceous species (including Plantago virginica,Solidago canadensis,Conyza canadensis and Erigeron philadelphicus were obtained by using EPSON V7000 scanner with different resolutions.Root morphological parameters including root length,diameter,volume and area were determined by using a WinRhizo root analyzing software.The results show a distinct influence of image resolution on root morphological parameter.For different herbaceous species,the optimal resolutions of root images,which would produce an acceptable precision with relative short time,vary with different species.For example,a resolution of 200 dpi was recommended for the root images of Plantago virginica and S.Canadensis, while 400 dpi for Conyza canadensis and Erigeron philadelphicus.
Examining the Simple View of Reading among Subgroups of Spanish-Speaking English Language Learners

Science.gov (United States)

Grimm, Ryan Ponce

2015-01-01

The Simple View of Reading (SVR; Gough & Tunmer, 1986; Hoover & Gough, 1990) has a longstanding history as a model of reading comprehension, but it has mostly been applied to native English speakers. The SVR posits reading comprehension is a function of the interaction between word-level reading skills and oral language skills. It has been…
For US Students, L2 Reading Comprehension Is Hard Because L2 Listening Comprehension Is Hard, Too

Science.gov (United States)

Sparks, Richard; Patton, Jon; Luebbers, Julie

2018-01-01

The Simple View of Reading (SVR) model posits that reading is the product of word decoding and language comprehension and that oral language (listening) comprehension is the best predictor of reading comprehension once word-decoding skill has been established. The SVR model also proposes that there are good readers and three types of poor…
A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

Science.gov (United States)

Tonutti, Michele; Gras, Gauthier; Yang, Guang-Zhong

2017-07-01

Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models. A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh. The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm. The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. Copyright © 2017 Elsevier B.V. All rights reserved.
A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks

KAUST Repository

Rached, Nadhir B.; Ghazzai, Hakim; Kadri, Abdullah; Alouini, Mohamed-Slim

2018-01-01

In this letter, we develop a time-varied probabilistic on/off switching planning method for cellular networks to reduce their energy consumption. It consists in a risk-aware optimization approach that takes into consideration the randomness of the user profile associated with each base station (BS). The proposed approach jointly determines (i) the instants of time at which the current active BS configuration must be updated due to an increase or decrease of the network traffic load, and (ii) the set of minimum BSs to be activated to serve the networks’ subscribers. Probabilistic metrics modeling the traffic profile variation are developed to trigger this dynamic on/off switching operation. Selected simulation results are then performed to validate the proposed algorithm for different system parameters.
A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks

KAUST Repository

Rached, Nadhir B.

2018-01-11

In this letter, we develop a time-varied probabilistic on/off switching planning method for cellular networks to reduce their energy consumption. It consists in a risk-aware optimization approach that takes into consideration the randomness of the user profile associated with each base station (BS). The proposed approach jointly determines (i) the instants of time at which the current active BS configuration must be updated due to an increase or decrease of the network traffic load, and (ii) the set of minimum BSs to be activated to serve the networks’ subscribers. Probabilistic metrics modeling the traffic profile variation are developed to trigger this dynamic on/off switching operation. Selected simulation results are then performed to validate the proposed algorithm for different system parameters.
Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

International Nuclear Information System (INIS)

Che Jinxing; Wang Jianzhou

2010-01-01

In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Investigation of the influence of image reconstruction filter and scan parameters on operation of automatic tube current modulation systems for different CT scanners

International Nuclear Information System (INIS)

Sookpeng, Supawitoo; Martin, Colin J.; Gentle, David J.

2015-01-01

Variation in the user selected CT scanning parameters under automatic tube current modulation (ATCM) between hospitals has a substantial influence on the radiation doses and image quality for patients. The aim of this study was to investigate the effect of changing image reconstruction filter and scan parameter settings on tube current, dose and image quality for various CT scanners operating under ATCM. The scan parameters varied were pitch factor, rotation time, collimator configuration, kVp, image thickness and image filter convolution (FC) used for reconstruction. The Toshiba scanner varies the tube current to achieve a set target noise. Changes in the FC setting and image thickness for the first reconstruction were the major factors affecting patient dose. A two-step change in FC from smoother to sharper filters doubles the dose, but is counterbalanced by an improvement in spatial resolution. In contrast, Philips and Siemens scanners maintained tube current values similar to those for a reference image and patient, and the tube current only varied slightly for changes in individual CT scan parameters. The selection of a sharp filter increased the image noise, while use of iDose iterative reconstruction reduced the noise. Since the principles used by CT manufacturers for ATCM vary, it is important that parameters which affect patient dose and image quality for each scanner are made clear to operator to aid in optimisation. (authors)
Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

Science.gov (United States)

Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

2018-03-01

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.
Analysis of design parameters for crosstalk cancellation filters applied to different loudspeaker configurations

DEFF Research Database (Denmark)

Parodi, Yesenia Lacouture

2008-01-01

Several approaches to render binaural signals through loudspeakers have been proposed previously. Some studies had focused on the optimum loudspeaker arrangement while others had proposed efficient filters. However, to our knowledge, the identification of optimal parameters for inverse methods ap...... loudspeaker arrangements. Least square approximations in frequency and time domain are evaluated along with a crosstalk canceler based on minimum-phase approximation. Filter parameters, such as length and regularization, are varied and simulated for different span and elevation angles....
Can we predict uranium bioavailability based on soil parameters? Part 1: effect of soil parameters on soil solution uranium concentration.

Science.gov (United States)

Vandenhove, H; Van Hees, M; Wouters, K; Wannijn, J

2007-01-01

Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for (238)U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K(d), L kg(-1)) and the organic matter content (R(2)=0.70) and amorphous Fe content (R(2)=0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH=6, log(K(d)) was linearly related with pH [log(K(d))=-1.18 pH+10.8, R(2)=0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex.
New Delay-Dependent Robust Exponential Stability Criteria of LPD Neutral Systems with Mixed Time-Varying Delays and Nonlinear Perturbations

Directory of Open Access Journals (Sweden)

Sirada Pinjai

2013-01-01

Full Text Available This paper is concerned with the problem of robust exponential stability for linear parameter-dependent (LPD neutral systems with mixed time-varying delays and nonlinear perturbations. Based on a new parameter-dependent Lyapunov-Krasovskii functional, Leibniz-Newton formula, decomposition technique of coefficient matrix, free-weighting matrices, Cauchy’s inequality, modified version of Jensen’s inequality, model transformation, and linear matrix inequality technique, new delay-dependent robust exponential stability criteria are established in terms of linear matrix inequalities (LMIs. Numerical examples are given to show the effectiveness and less conservativeness of the proposed methods.
The role of biochemical variations and genotype testing in determining the virological response of patients infected with hepatitis C virus

Directory of Open Access Journals (Sweden)

Abid Shoukat

2018-01-01

Full Text Available Background: In hepatitis C virus (HCV, infection viral and IL28B genotype along with many clinical and biochemical factors can influence response rates to pegylated interferon plus ribavirin (Peg-IFN-a/R therapy and progression to chronic hepatitis C (CHC. Aims: The present study was conducted to determine the effect of biochemical and risk factors on treatment outcome in CHC patients in relation to their viral and host genotype. Settings and Design: The present study was a prospective Pe- IFN efficacy study consisting of Peg-IFN-a/R therapy for 24–48 weeks including 250 HCV infected patients. Materials and Methods: Biochemical parameters were determined by Beckman Coulter AU680 automated analyzer. HCV and Interleukin 28B (IL28B genotyping were carried out by polymerase chain reaction-restriction fragment length polymorphism and viral load was determined by quantitative real-time PCR. Results: Wild outnumbered the variant genotypes in rs 12979860, rs 12980275, and rs 8099917 SNP of IL28B gene. Sustained virological response (SVR SVR and viral genotype were significantly associated with age, hepatic steatosis, low-grade varices, and serum aspartate transaminase levels (at the end of treatment (P < 0.05. In addition, SVR was significantly influenced by body mass index (BMI, insulin resistance, serum low-density lipoprotein , and ferritin levels (P < 0.05. Viral genotype 1 infected patients had higher serum cholesterol and triglyceride levels (P < 0.05. Conclusions: Although the IL28B sequence variation is the major factor that can influence response rates to antiviral therapy, viral and biochemical factors also have a definite role to play in the diagnosis, etiology, and treatment outcome in HCV-infected patients.
A Synoptic Climatology of Combined Severe/Weather/Flash Flood Events

Science.gov (United States)

Pallozzi, Kyle J.

Classical forms of severe weather such as tornadoes, damaging convective wind gusts, and large hail, as well as flash flooding events, all have potentially large societal impacts. This impact is further magnified when these hazards occur simultaneously in time and space. A major challenge for operational forecasters is how to accurately predict the occurrence of combined storm hazards, and how to communicate the associated multiple threat hazards to the public. A seven-year climatology (2009-2015) of combined severe weather/flash flooding (SVR/FF) events across the contiguous United States was developed in attempt to study the combined SVR/FF event hazards further. A total of 211 total cases were identified and sub-divided into seven subcategories based on their convective morphology and meteorological characteristics. Heatmaps of event report frequency were created to extract spatial, seasonal and interannual patterns in SVR/FF event activity. Diurnal trends were examined from time series plots of tornado, hail, wind and flash flood/flood reports. Event-centered composites of environmental variables were created for each subcategory from 13 km RUC/RAP analyses. Representative cases studies were conducted for each subcategory. A "ring of fire" with the highest levels of SVR/FF event activity was noted across the central United States. SVR/FF events were least common in the Southeast, High Plains, and Northern Plains. Enhanced SVR/FF activity reflected contributions from synoptic events during the cool and shoulder seasons over the Lower Mississippi, Arkansas and Tennessee Valleys, and MCS activity during the warm season over the lower Great Plains, and the Upper Mississippi, Missouri and Ohio River Valleys. Results from the composite analyses indicated that relatively high values of CAPE, surface-500 hPa shear and precipitable water were observed for all subcategories. Case studies show that many high-end SVR/FF events featured slow-moving, or quasi
Kinetic parameter estimation from SPECT cone-beam projection measurements

International Nuclear Information System (INIS)

Huesman, Ronald H.; Reutter, Bryan W.; Zeng, G. Larry; Gullberg, Grant T.

1998-01-01

Kinetic parameters are commonly estimated from dynamically acquired nuclear medicine data by first reconstructing a dynamic sequence of images and subsequently fitting the parameters to time-activity curves generated from regions of interest overlaid upon the image sequence. Biased estimates can result from images reconstructed using inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system. If the SPECT data are acquired using cone-beam collimators wherein the gantry rotates so that the focal point of the collimators always remains in a plane, additional biases can arise from images reconstructed using insufficient, as well as truncated, projection samples. To overcome these problems we have investigated the estimation of kinetic parameters directly from SPECT cone-beam projection data by modelling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated chest image volume, kinetic parameters were estimated for simple one-compartment models for four myocardial regions of interest. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated cone-beam data had biases ranging between 3-26% and 0-28%, respectively. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Statistical uncertainties of parameter estimates for 10 000 000 events ranged between 0.2-9% for the uptake parameters and between 0.3-6% for the washout parameters. (author)

Neutrino oscillation parameter sampling with MonteCUBES

Science.gov (United States)

Blennow, Mattias; Fernandez-Martinez, Enrique

2010-01-01

used in GLoBES [1,2]. Solution method: MonteCUBES is written as a plug-in to the GLoBES software [1,2] and provides the necessary methods to perform Markov Chain Monte Carlo sampling of the parameter space. This allows an efficient sampling of the parameter space and has a complexity which does not grow exponentially with the parameter space dimension. The integration of the MonteCUBES package with the GLoBES software makes sure that the experimental definitions already in use by the community can also be used with MonteCUBES, while also lowering the learning threshold for users who already know GLoBES. Additional comments: A Matlab GUI for interpretation of results is included in the distribution. Running time: The typical running time varies depending on the dimensionality of the parameter space, the complexity of the experiment, and how well the parameter space should be sampled. The running time for our simulations [3] with 15 free parameters at a Neutrino Factory with O(10) samples varied from a few hours to tens of hours. References:P. Huber, M. Lindner, W. Winter, Comput. Phys. Comm. 167 (2005) 195, hep-ph/0407333. P. Huber, J. Kopp, M. Lindner, M. Rolinec, W. Winter, Comput. Phys. Comm. 177 (2007) 432, hep-ph/0701187. S. Antusch, M. Blennow, E. Fernandez-Martinez, J. Lopez-Pavon, arXiv:0903.3986 [hep-ph].
Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Science.gov (United States)

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-09-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.
Neutron quality parameters versus energy below 4 MeV from microdosimetric calculations

International Nuclear Information System (INIS)

Stinchcomb, T.G.; Borak, T.B.

1983-01-01

Charged-particle production by neutrons and the resulting energy-deposition spectra in micron-sized spheres of tissue of varying diameters were calculated from thermal energies to 4 MeV. These data were used to obtain dose-average values of several quality-indicating parameters as functions of neutron energy and of tissue sphere diameter. The contrast among the parameters is shown and discussed. Applications are made to two neutron spectra, one a fission spectrum in air and the other a moderated spectrum at the center of an irradiated cube of water
Vertical distribution of organochlorine pesticides in humus along Alpine altitudinal profiles in relation to ambiental parameters

Energy Technology Data Exchange (ETDEWEB)

Kirchner, M., E-mail: kirchner@helmholtz-muenchen.d [Helmholtz Zentrum Muenchen, GmbH, Institutes of Ecological Chemistry, Developmental Genetics and Soil Ecology, Ingolstaedter Landstrasse 1, D-85764 Neuherberg (Germany); Faus-Kessler, T.; Jakobi, G.; Levy, W.; Henkelmann, B.; Bernhoeft, S.; Kotalik, J.; Zsolnay, A. [Helmholtz Zentrum Muenchen, GmbH, Institutes of Ecological Chemistry, Developmental Genetics and Soil Ecology, Ingolstaedter Landstrasse 1, D-85764 Neuherberg (Germany); Bassan, R. [Regional Agency for Environmental Prevention and Protection of Veneto (Italy); Belis, C. [Regional Agency for Environmental Protection of Lombardy (Italy); Kraeuchi, N. [Swiss Federal Institute for Forest, Snow and Landscape Research (Switzerland); Moche, W. [Federal Environment Agency Ltd. (Austria); Simoncic, P. [Slovenian Forestry Institute (Slovenia); Uhl, M.; Weiss, P. [Federal Environment Agency Ltd. (Austria); Schramm, K.-W. [Helmholtz Zentrum Muenchen, GmbH, Institutes of Ecological Chemistry, Developmental Genetics and Soil Ecology, Ingolstaedter Landstrasse 1, D-85764 Neuherberg (Germany)

2009-12-15

In forest soils along vertical profiles located in different parts of the Alps, concentrations of persistent organic pollutants (POPs), namely organochlorine pesticides (OCPs) like dichlorodiphenyltrichloroethanes (DDTs), hexachlorobenzene (HCB), hexachlorocyclohexanes (HCH), heptachlor, aldrin, dieldrin and mirex, were measured. Though local characteristics of the sites are influenced by numerous factors like orographic and meteorological parameters, forest stand characteristics and humus parameters, we ascertained a marked vertical increase of concentrations of some organochlorine compounds in the soil. On the basis of climatological values of each site, we found that the contamination increase with altitude can be ascribed to a certain 'cold condensation effect'. In addition, the perennial atmospheric deposition of POPs is controlled by precipitation. Other key parameters explaining the accumulation of POPs are the soil organic carbon stocks, the turnover times, the re-volatilisation and degradation processes, which vary with altitude. - Caused by temperature-dependent processes regarding deposition, re-volatilization and decomposition of POPs, the concentration of organochlorine pesticides varies in the Alpine region with altitude.
Effect of economic parameters on power generation expansion planning

International Nuclear Information System (INIS)

Sevilgen, Sueleyman Hakan; Hueseyin Erdem, Hasan; Cetin, Burhanettin; Volkan Akkaya, Ali; Dagdas, Ahmet

2005-01-01

The increasing consumption of electricity within time forces countries to build additional power plants. Because of technical and economic differences of the additional power plants, economic methodologies are used to determine the best technology for the additional capacity. The annual levelized cost method is used for this purpose, and the technology giving the minimum value for the additional load range is chosen. However, the economic parameters such as interest rate, construction escalation, fuel escalation, maintenance escalation and discount factor can affect the annual levelized cost considerably and change the economic range of the plants. Determining the values of the economical parameters in the future is very difficult, especially in developing countries. For this reason, the analysis of the changing rates of the mentioned values is of great importance for the planners of the additional capacity. In this study, the changing rates of the economic parameters that influence the annual levelized cost of the alternative power plant types are discussed. The alternative power plants considered for the electricity generation sector of Turkey and the economic parameters dominating each plant type are determined. It is clearly seen that the annual levelized cost for additional power plants varies with the economic parameters. The results show that the economic parameters variation has to be taken into consideration in electricity generation planning
LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
EX VIVO STUDY OF QUANTITATIVE ULTRASOUND PARAMETERS IN FATTY RABBIT LIVERS

Science.gov (United States)

Ghoshal, Goutam; Lavarello, Roberto J.; Kemmerer, Jeremy P.; Miller, Rita J.; Oelze, Michael L.

2012-01-01

Nonalcoholic fatty liver disease (NAFLD) affects more than 30% of Americans, and with increasing problems of obesity in the United States, NAFLD is poised to become an even more serious medical concern. At present, accurate classification of steatosis (fatty liver) represents a significant challenge. In this study, the use of high-frequency (8 to 25 MHz) quantitative ultrasound (QUS) imaging to quantify fatty liver was explored. QUS is an imaging technique that can be used to quantify properties of tissue giving rise to scattered ultrasound. The changes in the ultrasound properties of livers in rabbits undergoing atherogenic diets of varying durations were investigated using QUS. Rabbits were placed on a special fatty diet for 0, 3, or 6 weeks. The fattiness of the livers was quantified by estimating the total lipid content of the livers. Ultrasonic properties, such as speed of sound, attenuation, and backscatter coefficients, were estimated in ex vivo rabbit liver samples from animals that had been on the diet for varying periods. Two QUS parameters were estimated based on the backscatter coefficient: effective scatterer diameter (ESD) and effective acoustic concentration (EAC), using a spherical Gaussian scattering model. Two parameters were estimated based on the backscattered envelope statistics (the k parameter and the μ parameter) according to the homodyned K distribution. The speed of sound decreased from 1574 to 1565 m/s and the attenuation coefficient increased from 0.71 to 1.27 dB/cm/MHz, respectively, with increasing fat content in the liver. The ESD decreased from 31 to 17 μm and the EAC increased from 38 to 63 dB/cm3 with increasing fat content in the liver. A significant increase in the μ parameter from 0.18 to 0.93 scatterers/mm3 was observed with increasing fat content in the liver samples. The results of this study indicate that QUS parameters are sensitive to fat content in the liver. PMID:23062376
Marijuana smoking: effects of varying puff volume and breathhold duration.

Science.gov (United States)

Azorlosa, J L; Greenwald, M K; Stitzer, M L

1995-02-01

Two studies were conducted to quantify biological and behavioral effects resulting from exposure to controlled doses of marijuana smoke. In one study, puff volume (30, 60 and 90 ml) and in a second study, breathhold duration (0, 10 and 20 sec) were systematically varied while holding constant other smoking topography parameters (number of puffs = 10, interpuff interval = 60 sec and inhalation volume = 25% of vital capacity). Each study also varied levels of delta 9-tetrahydro-cannabinol marijuana cigarette content (1.75% and 3.55%). Regular marijuana users served as subjects (n = 7 in each experiment). Subjects smoked 10 puffs in each of six sessions; a seventh, nonsmoking session (all measures recorded at the same times as in active smoking sessions) served as a control. Variations in puff volume produced significant dose-related changes in postsmoking plasma delta 9-tetrahydro-cannabinol levels, carbon monoxide boost and subjective effects (e.g., "high"). In contrast, breathholding for 10 or 20 sec versus 0 sec increased plasma delta 9-tetrahydro-cannabinol levels but not CO boost or subjective effects. Task performance measures were not reliably influenced by marijuana smoke exposure within the dosing ranges examined. These findings confirm the utility of the controlled smoking technology, support the notion that cumulative puff volume systematically influences biological exposure and subjective effects, but cast doubt on the common belief that prolonged breathholding of marijuana smoke enhances classical subjective effects associated with its reinforcing value in humans.
Study of selected phenotype switching strategies in time varying environment

Energy Technology Data Exchange (ETDEWEB)

Horvath, Denis, E-mail: horvath.denis@gmail.com [Centre of Interdisciplinary Biosciences, Institute of Physics, Faculty of Science, P.J. Šafárik University in Košice, Jesenná 5, 040 01 Košice (Slovakia); Brutovsky, Branislav, E-mail: branislav.brutovsky@upjs.sk [Department of Biophysics, Institute of Physics, P.J. Šafárik University in Košice, Jesenná 5, 040 01 Košice (Slovakia)

2016-03-22

Population heterogeneity plays an important role across many research, as well as the real-world, problems. The population heterogeneity relates to the ability of a population to cope with an environment change (or uncertainty) preventing its extinction. However, this ability is not always desirable as can be exemplified by an intratumor heterogeneity which positively correlates with the development of resistance to therapy. Causation of population heterogeneity is therefore in biology and medicine an intensively studied topic. In this paper the evolution of a specific strategy of population diversification, the phenotype switching, is studied at a conceptual level. The presented simulation model studies evolution of a large population of asexual organisms in a time-varying environment represented by a stochastic Markov process. Each organism disposes with a stochastic or nonlinear deterministic switching strategy realized by discrete-time models with evolvable parameters. We demonstrate that under rapidly varying exogenous conditions organisms operate in the vicinity of the bet-hedging strategy, while the deterministic patterns become relevant as the environmental variations are less frequent. Statistical characterization of the steady state regimes of the populations is done using the Hellinger and Kullback–Leibler functional distances and the Hamming distance. - Highlights: • Relation between phenotype switching and environment is studied. • The Markov chain Monte Carlo based model is developed. • Stochastic and deterministic strategies of phenotype switching are utilized. • Statistical measures of the dynamic heterogeneity reveal universal properties. • The results extend to higher lattice dimensions.
Study of selected phenotype switching strategies in time varying environment

International Nuclear Information System (INIS)

Horvath, Denis; Brutovsky, Branislav

2016-01-01

Population heterogeneity plays an important role across many research, as well as the real-world, problems. The population heterogeneity relates to the ability of a population to cope with an environment change (or uncertainty) preventing its extinction. However, this ability is not always desirable as can be exemplified by an intratumor heterogeneity which positively correlates with the development of resistance to therapy. Causation of population heterogeneity is therefore in biology and medicine an intensively studied topic. In this paper the evolution of a specific strategy of population diversification, the phenotype switching, is studied at a conceptual level. The presented simulation model studies evolution of a large population of asexual organisms in a time-varying environment represented by a stochastic Markov process. Each organism disposes with a stochastic or nonlinear deterministic switching strategy realized by discrete-time models with evolvable parameters. We demonstrate that under rapidly varying exogenous conditions organisms operate in the vicinity of the bet-hedging strategy, while the deterministic patterns become relevant as the environmental variations are less frequent. Statistical characterization of the steady state regimes of the populations is done using the Hellinger and Kullback–Leibler functional distances and the Hamming distance. - Highlights: • Relation between phenotype switching and environment is studied. • The Markov chain Monte Carlo based model is developed. • Stochastic and deterministic strategies of phenotype switching are utilized. • Statistical measures of the dynamic heterogeneity reveal universal properties. • The results extend to higher lattice dimensions.
Hybrid Corporate Performance Prediction Model Considering Technical Capability

Directory of Open Access Journals (Sweden)

Joonhyuck Lee

2016-07-01

Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
Prediction of the Sodium Void Reactivity in the Metal-fueled SFR Using the ENDF/B-VII.0 Library

Energy Technology Data Exchange (ETDEWEB)

Yun, Sunghwan; Lim, Jae-Yong [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

The SVR (Sodium Void Reactivity) is one of the most important parameters in SFR (Sodium-cooled Fast Reactor) safety analysis. In this paper, to estimate the error of the SVR in metal-fueled SFR, three physics experiments named as BFS-75-1, BFS-109-2A, and BFS-84-1 were examined using recent cross-section library, ENDF/B-VII.0 and the MCNP code. In the MCNP6 calculation, two million histories/generation with 50 inactive/300 active generations are used with the continuous-energy ENDF/B-VII.0 library. We expect that accuracy of total cross-section of the sodium may play a dominant role in errors of SVRs at core peripheral and sodium plenum regions, whereas accuracy of capture cross-section of the sodium may play a dominant role for the results in errors of SVRs at core central region. In addition, capture cross-sections of the sodium in the ENDF/B-VII.0, the JEFF-3.2, and the JENDL-4.0 libraries show significant differences between each other, while total cross-sections of sodium in three libraries show good agreement.
Laryngeal High-Speed Videoendoscopy: Sensitivity of Objective Parameters towards Recording Frame Rate

Directory of Open Access Journals (Sweden)

Anne Schützenberger

2016-01-01

Full Text Available The current use of laryngeal high-speed videoendoscopy in clinic settings involves subjective visual assessment of vocal fold vibratory characteristics. However, objective quantification of vocal fold vibrations for evidence-based diagnosis and therapy is desired, and objective parameters assessing laryngeal dynamics have therefore been suggested. This study investigated the sensitivity of the objective parameters and their dependence on recording frame rate. A total of 300 endoscopic high-speed videos with recording frame rates between 1000 and 15 000 fps were analyzed for a vocally healthy female subject during sustained phonation. Twenty parameters, representing laryngeal dynamics, were computed. Four different parameter characteristics were found: parameters showing no change with increasing frame rate; parameters changing up to a certain frame rate, but then remaining constant; parameters remaining constant within a particular range of recording frame rates; and parameters changing with nearly every frame rate. The results suggest that (1 parameter values are influenced by recording frame rates and different parameters have varying sensitivities to recording frame rate; (2 normative values should be determined based on recording frame rates; and (3 the typically used recording frame rate of 4000 fps seems to be too low to distinguish accurately certain characteristics of the human phonation process in detail.
Time-varying surface electromyography topography as a prognostic tool for chronic low back pain rehabilitation.

Science.gov (United States)

Hu, Yong; Kwok, Jerry Weilun; Tse, Jessica Yuk-Hang; Luk, Keith Dip-Kei

2014-06-01

Nonsurgical rehabilitation therapy is a commonly used strategy to treat chronic low back pain (LBP). The selection of the most appropriate therapeutic options is still a big challenge in clinical practices. Surface electromyography (sEMG) topography has been proposed to be an objective assessment of LBP rehabilitation. The quantitative analysis of dynamic sEMG would provide an objective tool of prognosis for LBP rehabilitation. To evaluate the prognostic value of quantitative sEMG topographic analysis and to verify the accuracy of the performance of proposed time-varying topographic parameters for identifying the patients who have better response toward the rehabilitation program. A retrospective study of consecutive patients. Thirty-eight patients with chronic nonspecific LBP and 43 healthy subjects. The accuracy of the time-varying quantitative sEMG topographic analysis for monitoring LBP rehabilitation progress was determined by calculating the corresponding receiver-operating characteristic (ROC) curves. Physiologic measure was the sEMG during lumbar flexion and extension. Patients who suffered from chronic nonspecific LBP without the history of back surgery and any medical conditions causing acute exacerbation of LBP during the clinical test were enlisted to perform the clinical test during the 12-week physiotherapy (PT) treatment. Low back pain patients were classified into two groups: "responding" and "nonresponding" based on the clinical assessment. The responding group referred to the LBP patients who began to recover after the PT treatment, whereas the nonresponding group referred to some LBP patients who did not recover or got worse after the treatment. The results of the time-varying analysis in the responding group were compared with those in the nonresponding group. In addition, the accuracy of the analysis was analyzed through ROC curves. The time-varying analysis showed discrepancies in the root-mean-square difference (RMSD) parameters between the
A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery

International Nuclear Information System (INIS)

Ouyang, Minggao; Feng, Xuning; Han, Xuebing; Lu, Languang; Li, Zhe; He, Xiangming

2016-01-01

Highlights: • A dynamic capacity degradation model for large format Li-ion battery is proposed. • The change of the model parameters directly link with the degradation mechanisms. • The model can simulate the fading behavior of Li-ion battery under varying loads. • The model can help evaluate the longevity of a battery system under specific load. • The model can help predict the evolution of cell variations within a battery pack. - Abstract: The capacity degradation of the lithium ion battery should be well predicted during battery system design. Therefore, high-fidelity capacity degradation models that are suitable for the task of capacity prediction are required. This paper proposes a novel capacity degradation model that can simulate the degradation dynamics under varying working conditions for large-format lithium ion batteries. The degradation model is built based on a mechanistic and prognostic model (MPM) whose parameters are closely linked with the degradation mechanisms of lithium ion batteries. Chemical kinetics was set to drive the parameters of the MPM to change as capacity degradation continues. With the dynamic parameters of the MPM, the capacity predicted by the degradation model decreases as the cycle continues. Accelerated aging tests were conducted on three types of commercial lithium ion batteries to calibrate the capacity degradation model. The good fit with the experimental data indicates that the model can capture the degradation mechanisms well for different types of commercial lithium ion batteries. Furthermore, the calibrated model can be used to (1) evaluate the longevity of a battery system under a specific working load and (2) predict the evolution of cell variations within a battery pack when different cell works at different conditions. Correlated applications are discussed using the calibrated degradation model.
Seleção de variáveis em modelos matemáticos dos parâmetros de cultivo do camarão marinho Litopenaeus vannamei Selection of variables in mathematical models of culture parameters of marine shrimp Litopenaeus vannamei

Directory of Open Access Journals (Sweden)

Ady Marinho Bezerra

2007-03-01

Full Text Available O objetivo deste trabalho foi selecionar as variáveis de manejo do camarão marinho Litopenaeus vannamei que mais influenciaram nas variáveis-respostas ao cultivo (produção, produtividade, peso final e taxa de sobrevivência, em modelos matemáticos. O banco de dados foi composto por 83 cultivos, realizados no período de 2003 a 2005, obtidos de uma fazenda comercial localizada no litoral sul de Pernambuco. Para estimar os parâmetros dos modelos, utilizou-se a técnica dos mínimos quadrados. A seleção das variáveis foi realizada com o processo "backward elimination" associado ao método de transformação de Box e Cox. A adequação das equações e os pressupostos de normalidade e homocedasticidade, para os erros, foram analisadas com base na análise de variância e análise de resíduo. É possível relacionar essas variáveis e estabelecer predições com as equações.The objective of this work was to select management variables of the marine shrimp Litopenaeus vannamei that most influenced culture variable responses (production, productivity, final weight and survival rate, in mathematical models. The database was composed of 83 cultures in the period of 2003 to 2005, obtained from a shrimp farm located in the South coast of Pernambuco. To estimate the parameters of the models it was used the technique of least square. The selection of variable was carried through the backward elimination process associated to the Box and Cox transformation. The adequacy of the equations and the hypothesis of normality and homogeneous variance for the errors were analyzed based on the analysis of variance and on the analysis of residuals. It is possible to correlate those variables and to establish predictions with the equations.
Matching Value Propositions with Varied Customer Needs

DEFF Research Database (Denmark)

Heikka, Eija-Liisa; Frandsen, Thomas; Hsuan, Juliana

2018-01-01

Organizations seek to manage varied customer segments using varied value propositions. The ability of a knowledge-intensive business service (KIBS) provider to formulate value propositions into attractive offerings to varied customers becomes a competitive advantage. In this specific business based...... on often highly abstract service offerings, this requires the provider to have a clear overview of its knowledge and resources and how these can be configured to obtain the desired customization of services. Hence, the purpose of this paper is to investigate how a KIBS provider can match value propositions...... with varied customer needs utilizing service modularity. To accomplish this purpose, a qualitative multiple case study is organized around 5 projects allowing within-case and cross-case comparisons. Our findings describe how through the configuration of knowledge and resources a sustainable competitive...
Wavelength detection in FBG sensor networks using least squares support vector regression

Science.gov (United States)

Chen, Jing; Jiang, Hao; Liu, Tundong; Fu, Xiaoli

2014-04-01

A wavelength detection method for a wavelength division multiplexing (WDM) fiber Bragg grating (FBG) sensor network is proposed based on least squares support vector regression (LS-SVR). As a kind of promising machine learning technique, LS-SVR is employed to approximate the inverse function of the reflection spectrum. The LS-SVR detection model is established from the training samples, and then the Bragg wavelength of each FBG can be directly identified by inputting the measured spectrum into the well-trained model. We also discuss the impact of the sample size and the preprocess of the input spectrum on the performance of the training effectiveness. The results demonstrate that our approach is effective in improving the accuracy for sensor networks with a large number of FBGs.
Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

Science.gov (United States)

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.
An analysis of periodic solutions of bi-directional associative memory networks with time-varying delays

International Nuclear Information System (INIS)

Cao Jinde; Jiang Qiuhao

2004-01-01

In this Letter, several sufficient conditions are derived for the existence and uniqueness of periodic oscillatory solution for bi-directional associative memory (BAM) networks with time-varying delays by employing a new Lyapunov functional and an elementary inequality, and all other solutions of the BAM networks converge exponentially to the unique periodic solution. These criteria are presented in terms of system parameters and have important leading significance in the design and applications of periodic neural circuits for delayed BAM. As an illustration, two numerical examples are worked out using the results obtained

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Qiankun Song

2007-06-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Cao Jinde

2007-01-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults

Directory of Open Access Journals (Sweden)

Jin Wang

2017-03-01

Full Text Available This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi–Sugeno fuzzy model. An interval type-II Takagi–Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of such method are verified by a series of the comparison of numerical simulation results.
The influence of different PAST-based subspace trackers on DaPT parameter estimation

Science.gov (United States)

Lechtenberg, M.; Götze, J.

2012-09-01

In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers. One class of these is based on the PAST algorithm. Our non-linear parameter estimation algorithm DaPT builds on-top of the ESPRIT algorithm. Evaluation of the different variants of the PAST algorithm shows which variant of the PAST algorithm is worthwhile in the context of frequency estimation.
Design of 2D time-varying vector fields.

Science.gov (United States)

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.
Response to ``Comment on `Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks''' [Chaos 17, 038101 (2007)

Science.gov (United States)

Yu, Wenwu; Cao, Jinde

2007-09-01

Parameter identification of dynamical systems from time series has received increasing interest due to its wide applications in secure communication, pattern recognition, neural networks, and so on. Given the driving system, parameters can be estimated from the time series by using an adaptive control algorithm. Recently, it has been reported that for some stable systems, in which parameters are difficult to be identified [Li et al., Phys Lett. A 333, 269-270 (2004); Remark 5 in Yu and Cao, Physica A 375, 467-482 (2007); and Li et al., Chaos 17, 038101 (2007)], and in this paper, a brief discussion about whether parameters can be identified from time series is investigated. From some detailed analyses, the problem of why parameters of stable systems can be hardly estimated is discussed. Some interesting examples are drawn to verify the proposed analysis.
A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise

Science.gov (United States)

Dai, Yu; Xue, Yuan; Zhang, Jianxun

2016-01-01

Impulsive noise caused by some random events has the main character of short rise-time and wide frequency spectrum range, so it has the potential to degrade the performance and reliability of the harmonic estimation. This paper focuses on the harmonic estimation procedure based on continuous wavelet transform (CWT) when the analyzed signal is corrupted by the impulsive noise. The digital CWT of both the time-varying sinusoidal signal and the impulsive noise are analyzed, and there are two cross ridges in the time-frequency plane of CWT, which are generated by the signal and the noise separately. In consideration of the amplitude of the noise and the number of the spike event, two inequalities are derived to provide limitations on the wavelet parameters. Based on the amplitude distribution of the noise, the optimal wavelet parameters determined by solving these inequalities are used to suppress the contamination of the noise, as well as increase the amplitude of the ridge corresponding to the signal, so the parameters of each harmonic component can be estimated accurately. The proposed procedure is applied to a numerical simulation and a bone vibration signal test giving satisfactory results of stationary and time-varying harmonic parameter estimation.
[Effectiveness of Self-efficacy Promoting Vestibular Rehabilitation Program for Patients with Vestibular Hypofunction].

Science.gov (United States)

Lee, Hyun Jung; Choi-Kwon, Smi

2016-10-01

In this study an examination was done of the effect of self-efficacy promoting vestibular rehabilitation (S-VR) on dizziness, exercise selfefficacy, adherence to vestibular rehabilitation (VR), subjective and objective vestibular function, vestibular compensation and the recurrence of dizziness in patients with vestibular hypofunction. This was a randomized controlled study. Data were collected 3 times at baseline, 4 and 8 weeks after beginning the intervention. Outcome measures were level of dizziness, exercise self-efficacy, and level of adherence to VR. Subjective and objective vestibular function, vestibular compensation and the recurrence of dizziness were also obtained. Data were analyzed using Windows SPSS 21.0 program. After 4 weeks of S-VR, there was no difference between the groups for dizziness, subjective and objective vestibular functions. However, exercise self-efficacy and adherence to VR were higher in the experimental group than in the control group. After 8 weeks of S-VR, dizziness (p=.018) exercise self-efficacy (pexercise self-efficacy, subjective vestibular function and adherence to VR. Objective vestibular function and vestibular compensation were also improved in the experimental group at the end of 8 weeks of S-VR.
DNBR Prediction Using a Support Vector Regression

International Nuclear Information System (INIS)

Yang, Heon Young; Na, Man Gyun

2008-01-01

PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values
Continuous administration of short-lived isotopes for evaluating dynamic parameters

International Nuclear Information System (INIS)

Selikson, M.

1985-01-01

In this paper it is shown that continuous but varying infusions (specifically, exponential infusions) of a short-lived radionuclide can be used to evaluate a wide range of dynamic parameters. The detector response to exponential infusions is derived. An example of an inert diffusible substrate for evaluating regional flow and a glucose model for evaluating regional metabolic rate are both worked out. The advantages of using exponential infusion methods are discussed
Monte Carlo parameter studies and uncertainty analyses with MCNP5

International Nuclear Information System (INIS)

Brown, F. B.; Sweezy, J. E.; Hayes, R.

2004-01-01

A software tool called mcnp p study has been developed to automate the setup, execution, and collection of results from a series of MCNP5 Monte Carlo calculations. This tool provides a convenient means of performing parameter studies, total uncertainty analyses, parallel job execution on clusters, stochastic geometry modeling, and other types of calculations where a series of MCNP5 jobs must be performed with varying problem input specifications. (authors)
Incremental Closed-loop Identification of Linear Parameter Varying Systems

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon; Trangbæk, Klaus

2011-01-01

, closed-loop system identification is more difficult than open-loop identification. In this paper we prove that the so-called Hansen Scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can be extended...
On structural reliability under time-varying multi-parameter loading

International Nuclear Information System (INIS)

Augusti, G.

1975-01-01

This paper intends to be a contribution towards the formulation of a procedure for the solution of the title problem that is at the same time correct and not too cumbersome for practical application. The problem is examined in detail and a number of possible alternative approaches to the solution discussed. Special attention is paid to the superimposition of loads of different origin and characteristics (e.g. long-term loads like the furniture and usual occupancy load in a building, and short-term loads like explosions, earthquakes, storms, etc.): it is recognized that a single procedure for all cases does not appear practical, and that, within a general framework, special methods must be devised according to the type of loads and structural responses. For instance, the superimposition of impulsive loads must be studied with reference to the response time of the structure. It is shown that usually, the statistics of extreme values are not sufficient for a correct study of superimposition: the instantaneous probability distributions of the load intensities are also required. (Auth.)
On structural reliability under time-varying multi-parameter loading

International Nuclear Information System (INIS)

Augusti, G.

1975-01-01

Special attention will be paid to the superimposition of loads of different origin and characteristics (e.g. long-term loads like the furniture and usual occupancy load in a building and short-term loads like explosions, earthquakes, storms, etc.): it will be recognized that a single procedure for all cases does not appear practical, and that, within a general framework special method must be devised according to the type of loads and structural responses. For instance, the superimposition of impulsive loads must be studied with reference to the response time of the structure. It will be shown that usually, the statistics of extreme values are not sufficient for a correct study of superimposition: the instantaneous probability distributions of the load intensities are also required. The results obtained with respect to the loads can be joined with previous results by Augusti and Baratta (see e.g. SMiRT-2 paper M7/8) on structural strength, for the evaluation of the probability of success (i.e. the reliability) of a structural design
Inflation and late-time acceleration in braneworld cosmological models with varying brane tension

International Nuclear Information System (INIS)

Wong, K.C.; Cheng, K.S.; Harko, T.

2010-01-01

Braneworld models with variable brane tension λ introduce a new degree of freedom that allows for evolving gravitational and cosmological constants, the latter being a natural candidate for dark energy. We consider a thermodynamic interpretation of the varying brane tension models, by showing that the field equations with variable λ can be interpreted as describing matter creation in a cosmological framework. The particle creation rate is determined by the variation rate of the brane tension, as well as by the brane-bulk energy-matter transfer rate. We investigate the effect of a variable brane tension on the cosmological evolution of the Universe, in the framework of a particular model in which the brane tension is an exponentially dependent function of the scale factor. The resulting cosmology shows the presence of an initial inflationary expansion, followed by a decelerating phase, and by a smooth transition towards a late accelerated de Sitter type expansion. The varying brane tension is also responsible for the generation of the matter in the Universe (reheating period). The physical constraints on the model parameters, resulting from the observational cosmological data, are also investigated. (orig.)
Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases

Energy Technology Data Exchange (ETDEWEB)

Snyder, Sandra F. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Arimescu, Carmen [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Napier, Bruce A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hay, Tristan R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2012-11-01

The GENII v2 code is used to estimate dose to individuals or populations from the release of radioactive materials into air or water. Numerous parameter values are required for input into this code. User-defined parameters cover the spectrum from chemical data, meteorological data, agricultural data, and behavioral data. This document is a summary of parameter values that reflect conditions in the United States. Reasonable regional and age-dependent data is summarized. Data availability and quality varies. The set of parameters described address scenarios for chronic air emissions or chronic releases to public waterways. Considerations for the special tritium and carbon-14 models are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.
Design of 2D Time-Varying Vector Fields

KAUST Repository

Chen, Guoning

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects. © 1995-2012 IEEE.
Design of 2D Time-Varying Vector Fields

KAUST Repository

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D.; Zhang, Eugene

2012-01-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects. © 1995-2012 IEEE.
Spatial modeling of limnological parameters in a solar saltwork of northeastern Brazil

Directory of Open Access Journals (Sweden)

Diógenes Félix da Silva Costa

2015-03-01

Full Text Available AimIn this research, we aimed to model limnological parameters in the Salina Unidos (Macau-Brazil using GIS technology. We hypothesized that in solar saltworks, the geochemical characteristics of the brines (i.e. the strong solution of salts vary considerably through the salt ponds circuit, in which drastic changes can damage the entire salt production.MethodsGeochemical parameters were monitored in seven sampling points distributed along the salt ponds circuit, during a complete cycle of salt production, i.e., from January to December 2007. The open source software Spring 5.1.6 was used to build, store, analyze and model the spatial distribution of the parameters.ResultsWe identified a spatial gradient of the salinity and temperature, with values increasing from evaporation ponds to concentration ponds, showing a relationship with the salt production. The parameters, depth, dissolved oxygen concentrations and total dissolved reactive phosphorus showed a decrease from the evaporation ponds towards the concentration ponds. Among the dissolved inorganic nitrogen forms analyzed (NH3-, NO2- and NO3-, nitrate was the predominant, namely in the concentration ponds, where it reached the highest concentrations. The concentration of chlorophyll awas higher in the initial and intermediate evaporation ponds, showing a distinct dynamics of in relation to other environmental variables.ConclusionsThe increased concentration of the analyzed limnological parameters, from the evaporation ponds towards the concentration ponds, evidenced a heterogeneous distribution varying significantly with season. The geochemical spatialization of brine, as illustrated by GIS approach, is very important for the conservation of these environments because this spatial heterogeneity can provide a high diversity of habitat types. This spatial analysis proved to be a practical tool for an adequate management of solar saltworks considering the environmental (ecosystem and the socio
River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

Science.gov (United States)

Baydaroğlu, Özlem; Koçak, Kasım; Duran, Kemal

2018-06-01

Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the future. This study presents a methodology for predicting river flow for the subsequent month based on the time series of observed monthly river flow with hybrid models of support vector regression (SVR). Monthly river flow over the period 1940-2012 observed for the Kızılırmak River in Turkey has been used for training the method, which then has been applied for predictions over a period of 3 years. SVR is a specific implementation of support vector machines (SVMs), which transforms the observed input data time series into a high-dimensional feature space (input matrix) by way of a kernel function and performs a linear regression in this space. SVR requires a special input matrix. The input matrix was produced by wavelet transforms (WT), singular spectrum analysis (SSA), and a chaotic approach (CA) applied to the input time series. WT convolutes the original time series into a series of wavelets, and SSA decomposes the time series into a trend, an oscillatory and a noise component by singular value decomposition. CA uses a phase space formed by trajectories, which represent the dynamics producing the time series. These three methods for producing the input matrix for the SVR proved successful, while the SVR-WT combination resulted in the highest coefficient of determination and the lowest mean absolute error.

Real-time prediction of respiratory motion using a cascade structure of an extended Kalman filter and support vector regression.

Science.gov (United States)

Hong, S-M; Bukhari, W

2014-07-07

The motion of thoracic and abdominal tumours induced by respiratory motion often exceeds 20 mm, and can significantly compromise dose conformality. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to the tumour with minimal normal tissue exposure by compensating for the tumour motion. This adaptive radiotherapy, however, requires the prediction of the tumour movement that can occur over the system latency period. In general, motion prediction approaches can be classified into two groups: model-based and model-free. Model-based approaches utilize a motion model in predicting respiratory motion. These approaches are computationally efficient and responsive to irregular changes in respiratory motion. Model-free approaches do not assume an explicit model of motion dynamics, and predict future positions by learning from previous observations. Artificial neural networks (ANNs) and support vector regression (SVR) are examples of model-free approaches. In this article, we present a prediction algorithm that combines a model-based and a model-free approach in a cascade structure. The algorithm, which we call EKF-SVR, first employs a model-based algorithm (named LCM-EKF) to predict the respiratory motion, and then uses a model-free SVR algorithm to estimate and correct the error of the LCM-EKF prediction. Extensive numerical experiments based on a large database of 304 respiratory motion traces are performed. The experimental results demonstrate that the EKF-SVR algorithm successfully reduces the prediction error of the LCM-EKF, and outperforms the model-free ANN and SVR algorithms in terms of prediction accuracy across lookahead lengths of 192, 384, and 576 ms.
Real-time prediction of respiratory motion using a cascade structure of an extended Kalman filter and support vector regression

International Nuclear Information System (INIS)

Hong, S-M; Bukhari, W

2014-01-01

The motion of thoracic and abdominal tumours induced by respiratory motion often exceeds 20 mm, and can significantly compromise dose conformality. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to the tumour with minimal normal tissue exposure by compensating for the tumour motion. This adaptive radiotherapy, however, requires the prediction of the tumour movement that can occur over the system latency period. In general, motion prediction approaches can be classified into two groups: model-based and model-free. Model-based approaches utilize a motion model in predicting respiratory motion. These approaches are computationally efficient and responsive to irregular changes in respiratory motion. Model-free approaches do not assume an explicit model of motion dynamics, and predict future positions by learning from previous observations. Artificial neural networks (ANNs) and support vector regression (SVR) are examples of model-free approaches. In this article, we present a prediction algorithm that combines a model-based and a model-free approach in a cascade structure. The algorithm, which we call EKF–SVR, first employs a model-based algorithm (named LCM–EKF) to predict the respiratory motion, and then uses a model-free SVR algorithm to estimate and correct the error of the LCM–EKF prediction. Extensive numerical experiments based on a large database of 304 respiratory motion traces are performed. The experimental results demonstrate that the EKF–SVR algorithm successfully reduces the prediction error of the LCM–EKF, and outperforms the model-free ANN and SVR algorithms in terms of prediction accuracy across lookahead lengths of 192, 384, and 576 ms. (paper)
Thermal study of bare tips with various system parameters and incision sizes.

Science.gov (United States)

Osher, Robert H; Injev, Valentine P

2006-05-01

To identify major and minor surgeon-controlled parameters that affect incision temperature when performing microincision lens removal using the Alcon Infiniti Vision System. In vitro research and development laboratory, Alcon Research, Irvine, California, USA. Phacoemulsification was performed in eye-bank cadaver eyes and the following parameters evaluated: incision, duty cycle, ultrasound (US) power, aspiration flow rate (AFR), vacuum, pulse, bottle height and balanced salt solution temperature, and tip design/size. Each parameter was varied while the others remained constant. The resulting temperature of the incision and US tip was measured using a thermal camera. Major contributors to elevated incision temperature included incision size, US power, duty cycle, AFR, vacuum setting, tip design, and presence of an ophthalmic viscosurgical device (OVD). Minor contributors included pulse frequency, bottle height, and temperature of the infusate. Microincision lens removal can be performed at safe temperatures with the knowledgeable selection of surgeon-controlled parameters.
Time-varying Crash Risk

DEFF Research Database (Denmark)

Christoffersen, Peter; Feunoua, Bruno; Jeon, Yoontae

We estimate a continuous-time model with stochastic volatility and dynamic crash probability for the S&P 500 index and find that market illiquidity dominates other factors in explaining the stock market crash risk. While the crash probability is time-varying, its dynamic depends only weakly on re...
Time varying voltage combustion control and diagnostics sensor

Science.gov (United States)

Chorpening, Benjamin T [Morgantown, WV; Thornton, Jimmy D [Morgantown, WV; Huckaby, E David [Morgantown, WV; Fincham, William [Fairmont, WV

2011-04-19

A time-varying voltage is applied to an electrode, or a pair of electrodes, of a sensor installed in a fuel nozzle disposed adjacent the combustion zone of a continuous combustion system, such as of the gas turbine engine type. The time-varying voltage induces a time-varying current in the flame which is measured and used to determine flame capacitance using AC electrical circuit analysis. Flame capacitance is used to accurately determine the position of the flame from the sensor and the fuel/air ratio. The fuel and/or air flow rate (s) is/are then adjusted to provide reduced flame instability problems such as flashback, combustion dynamics and lean blowout, as well as reduced emissions. The time-varying voltage may be an alternating voltage and the time-varying current may be an alternating current.
Ledipasvir/sofosbuvir for treatment of hepatitis C virus in sofosbuvir-experienced, NS5A treatment-naïve patients: Findings from two randomized trials.

Science.gov (United States)

Tam, Edward; Luetkemeyer, Anne F; Mantry, Parvez S; Satapathy, Sanjaya K; Ghali, Peter; Kang, Minhee; Haubrich, Richard; Shen, Xianlin; Ni, Liyun; Camus, Gregory; Copans, Amanda; Rossaro, Lorenzo; Guyer, Bill; Brown, Robert S

2018-06-01

We report data from two similarly designed studies that evaluated the efficacy, safety, and optimal duration of ledipasvir/sofosbuvir (LDV/SOF) ± ribavirin (RBV) for retreatment of chronic hepatitis C virus (HCV) in individuals who failed to achieve sustained virological response (SVR) with prior SOF-based, non-NS5A inhibitor-containing regimens. The RESCUE study enrolled HCV mono-infected adults with genotype (GT) 1 or 4. Non-cirrhotic participants were randomized to 12 weeks of LDV/SOF or LDV/SOF + RBV. Compensated cirrhotic participants were randomized to LDV/SOF + RBV (12 weeks) or LDV/SOF (24 weeks). The AIDS Clinical Trials Group A5348 study randomized genotype 1 adults with HCV/HIV co-infection to LDV/SOF + RBV (12 weeks) or LDV/SOF (24 weeks). Both studies used SVR at 12 weeks post-treatment (SVR12) as the primary endpoint. In the RESCUE study, 82 participants were randomized and treated, and all completed treatment. Overall, SVR12 was 88% (72/82); 81-100% in non-cirrhotic participants treated with LDV/SOF or LDV/SOF + RBV for 12 weeks and 80-92% in cirrhotic participants treated with LDV/SOF + RBV for 12 weeks or LDV/SOF for 24 weeks. Adverse events (AEs), mostly mild-to-moderate in severity, were experienced by 78% of participants, with headache and fatigue most frequently reported. One serious AE, not related to treatment, was observed. No premature discontinuations of study drug, or deaths occurred. In the A5348 study, seven participants were randomized (cirrhotic n = 1; GT1a n = 5) and all attained SVR12, with no serious AEs or premature discontinuations. In this SOF-experienced, NS5A inhibitor-naïve population, which included participants with cirrhosis or HCV/HIV co-infection, high SVR12 rates were achieved. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Ribavirin Concentrations Do Not Predict Sustained Virological Response in HIV/HCV-Coinfected Patients Treated with Ribavirin and Pegylated Interferon in the Swiss HIV Cohort Study.

Directory of Open Access Journals (Sweden)

Helen Kovari

Full Text Available Ribavirin (RBV is an essential component of most current hepatitis C (HCV treatment regimens and still standard of care in the combination with pegylated interferon (pegIFN to treat chronic HCV in resource limited settings. Study results in HIV/HCV-coinfected patients are contradicting as to whether RBV concentration correlates with sustained virological response (SVR.We included 262 HCV treatment naïve HIV/HCV-coinfected Swiss HIV Cohort Study (SHCS participants treated with RBV and pegIFN between 01.01.2001-01.01.2010, 134 with HCV genotype (GT 1/4, and 128 with GT 2/3 infections. RBV levels were measured retrospectively in stored plasma samples obtained between HCV treatment week 4 and end of therapy. Uni- and multivariable logistic regression analyses were used to evaluate the association between RBV concentration and SVR in GT 1/4 and GT 2/3 infections. The analyses were repeated stratified by treatment phase (week 4-12, 13-24, >24 and IL28B genotype (CC versus CT/TT.SVR rates were 35.1% in GT 1/4 and 70.3% in GT 2/3 infections. Overall, median RBV concentration was 2.0 mg/L in GT 1/4, and 1.9 mg/L in GT 2/3, and did not change significantly across treatment phases. Patients with SVR had similar RBV concentrations compared to patients without SVR in both HCV genotype groups. SVR was not associated with RBV levels ≥2.0 mg/L (GT 1/4, OR 1.19 [0.5-2.86]; GT 2/3, 1.94 [0.78-4.80] and ≥2.5 mg/L (GT 1/4, 1.56 [0.64-3.84]; GT 2/3 2.72 [0.85-8.73], regardless of treatment phase, and IL28B genotype.In HIV/HCV-coinfected patients treated with pegIFN/RBV, therapeutic drug monitoring of RBV concentrations does not enhance the chance of HCV cure, regardless of HCV genotype, treatment phase and IL28B genotype.
Immune biomarker differences and changes comparing HCV mono-infected, HIV/HCV co-infected, and HCV spontaneously cleared patients.

Directory of Open Access Journals (Sweden)

Lauren E Kushner

Full Text Available Immune biomarkers are implicated in HCV treatment response, fibrosis, and accelerated pathogenesis of comorbidities, though only D-dimer and C-reactive protein have been consistently studied. Few studies have evaluated HIV/HCV co-infection, and little longitudinal data exists describing a broader antiviral cytokine response.Fifty immune biomarkers were analyzed at baseline (BL and HCV end of treatment follow-up(FU time point using the Luminex 50-plex assay in plasma samples from 15 HCV-cleared, 24 HCV mono- and 49 HIV/HCV co-infected patients receiving antiretroviral treatment, who either did or did not receive pegylated-interferon/ribavirin HCV treatment. Biomarker levels were compared among spontaneous clearance patients, mono- and co-infected, untreated and HCV-treated, and sustained virologic responders (SVR and non-responders (NR at BL and FU using nonparametric analyses. A Bonferroni correction, adjusting for tests of 50 biomarkers, was used to reduce Type I error.Compared to HCV patients at BL, HIV/HCV patients had 22 significantly higher and 4 significantly lower biomarker levels, following correction for multiple testing. There were no significantly different BL levels when comparing SVR and NR in mono- or co-infected patients; however, FU levels changed considerably in co-infected patients, with seven becoming significantly higher and eight becoming significantly lower in SVR patients. Longitudinally between BL and FU, 13 markers significantly changed in co-infected SVR patients, while none significantly changed in co-infected NR patients. There were also no significant changes in longitudinal analyses of mono-infected patients achieving SVR or mono-infected and co-infected groups deferring treatment.Clear differences exist in pattern and quantity of plasma immune biomarkers among HCV mono-infected, HIV/HCV co-infected, and HCV-cleared patients; and with SVR in co-infected patients treated for HCV. Though >90% of patients were male and
EVALUATION OF EFFECTIVENESS OF ANTIVIRAL THERAPY FOR CHRONIC HEPATITIS C, CAUSED BY HCV GENOTYPE 6

Directory of Open Access Journals (Sweden)

D. A. Lioznov

2017-01-01

Full Text Available Objectives: Evaluating the effectiveness of 2 therapeutic schemes for chronic hepatitis C (genotype 6 which combined sofosbuvir and ribavirin, one of them also included pegylated interferon. Materials and methods: The study included 110 patients with chronic hepatitis C (genotype 6, who have undergone antiviral therapy (HTP in Hepatology Clinic inHo Chi Minh City,Vietnamfrom November 2015 to July 2016. 24 patients were treated by Pegylated interferon alfa-2a, ribavirin and sofosbuvir for 12 weeks, 86 patients – by sofosbuvir and ribavirin for 24 weeks. Non-interferon regimen was administered primarily to patients with contraindications to the use of interferon. To monitor the effectiveness of antiviral therapy, quantification of HCV RNA in serum was performed by PCR prior to treatment, at 4th, 12th or 24th week (depending on the observation group from the starting of treatment and at 12th, 24th week after completion of treatment. Results: All patients, who were treated with pegylated interferon, ribavirin and sofosbuvir, completed the full course of treatment and 100% of them are registered with sustained virological response at 12th and 24th week after the end of antiviral therapy (SVR-12 and SVR-24, respectively. In the group of patients, who treated with ribavirin and sofosbuvir, 97,7% of patients completed full course of treatment (SVR-12 was registered in 93% of patients, and SVR-24 – in 91,9% of patients. Of 75 patients without a history of HCC, SVR24 was registered in 74 people (98,7%, of 11 patients with HCC – in 5 patients (45,5%. SVR-24 was registered in 98% of patients with cirrhosis (F4 without HCC. Conclusion: The results can serve as a justification for the use of these schemes of antiviral therapy for special groups of patients and/or conditions when it is impossible to follow the latest recommendations, which will help to expand the access of patients to effective antiviral therapy for chronic hepatitis C.
A SVDD and K-Means Based Early Warning Method for Dual-Rotor Equipment under Time-Varying Operating Conditions

Directory of Open Access Journals (Sweden)

Zhinong Jiang

2018-01-01

Full Text Available Under frequently time-varying operating conditions, equipment with dual rotors like gas turbines is influenced by two rotors with different rotating speeds. Alarm methods of fixed threshold are unable to consider the influences of time-varying operating conditions. Hence, those methods are not suitable for monitoring dual-rotor equipment. An early warning method for dual-rotor equipment under time-varying operating conditions is proposed in this paper. The influences of time-varying rotating speeds of dual rotors on alarm thresholds have been considered. Firstly, the operating conditions are divided into several limited intervals according to rotating speeds of dual rotors. Secondly, the train data within each interval is processed by SVDD and the allowable ranges (i.e., the alarm threshold of the vibration are determined. The alarm threshold of each interval of operating conditions is obtained. The alarm threshold can be expressed as a sphere, whose controlling parameters are the coordinate of the center and the radius. Then, the cluster center of the test data, whose alarm state is to be judged, can be extracted through K-means. Finally, the alarm state can be obtained by comparing the cluster center with the corresponding sphere. Experiments are conducted to validate the proposed method.
Detection of dynamically varying interaural time differences

DEFF Research Database (Denmark)

Kohlrausch, Armin; Le Goff, Nicolas; Breebaart, Jeroen

2010-01-01

of fringes surrounding the probe is equal to the addition of the effects of the individual fringes. In this contribution, we present behavioral data for the same experimental condition, called dynamically varying ITD detection, but for a wider range of probe and fringe durations. Probe durations varied...
Hydrological model performance and parameter estimation in the wavelet-domain

Directory of Open Access Journals (Sweden)

B. Schaefli

2009-10-01

Full Text Available This paper proposes a method for rainfall-runoff model calibration and performance analysis in the wavelet-domain by fitting the estimated wavelet-power spectrum (a representation of the time-varying frequency content of a time series of a simulated discharge series to the one of the corresponding observed time series. As discussed in this paper, calibrating hydrological models so as to reproduce the time-varying frequency content of the observed signal can lead to different results than parameter estimation in the time-domain. Therefore, wavelet-domain parameter estimation has the potential to give new insights into model performance and to reveal model structural deficiencies. We apply the proposed method to synthetic case studies and a real-world discharge modeling case study and discuss how model diagnosis can benefit from an analysis in the wavelet-domain. The results show that for the real-world case study of precipitation – runoff modeling for a high alpine catchment, the calibrated discharge simulation captures the dynamics of the observed time series better than the results obtained through calibration in the time-domain. In addition, the wavelet-domain performance assessment of this case study highlights the frequencies that are not well reproduced by the model, which gives specific indications about how to improve the model structure.
Error propagation of partial least squares for parameters optimization in NIR modeling

Science.gov (United States)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.
Error propagation of partial least squares for parameters optimization in NIR modeling.

Science.gov (United States)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.
Sensitivity Analysis of Depletion Parameters for Heat Load Evaluation of PWR Spent Fuel Storage Pool

International Nuclear Information System (INIS)

Kim, In Young; Lee, Un Chul

2011-01-01

As necessity of safety re-evaluation for spent fuel storage facility has emphasized after the Fukushima accident, accuracy improvement of heat load evaluation has become more important to acquire reliable thermal-hydraulic evaluation results. As groundwork, parametric and sensitivity analyses of various storage conditions for Kori Unit 4 spent fuel storage pool and spent fuel depletion parameters such as axial burnup effect, operation history, and specific heat are conducted using ORIGEN2 code. According to heat load evaluation and parametric sensitivity analyses, decay heat of last discharged fuel comprises maximum 80.42% of total heat load of storage facility and there is a negative correlation between effect of depletion parameters and cooling period. It is determined that specific heat is most influential parameter and operation history is secondly influential parameter. And decay heat of just discharged fuel is varied from 0.34 to 1.66 times of average value and decay heat of 1 year cooled fuel is varied from 0.55 to 1.37 times of average value in accordance with change of specific power. Namely depletion parameters can cause large variation in decay heat calculation of short-term cooled fuel. Therefore application of real operation data instead of user selection value is needed to improve evaluation accuracy. It is expected that these results could be used to improve accuracy of heat load assessment and evaluate uncertainty of calculated heat load.
Thermodynamic curvature for a two-parameter spin model with frustration.

Science.gov (United States)

Ruppeiner, George; Bellucci, Stefano

2015-01-01

Microscopic models of realistic thermodynamic systems usually involve a number of parameters, not all of equal macroscopic relevance. We examine a decorated (1+3) Ising spin chain containing two microscopic parameters: a stiff parameter K mediating the long-range interactions, and a sloppy J operating within local spin groups. We show that K dominates the macroscopic behavior, with varying J having only a weak effect, except in regions where J brings about transitions between phases through its conditioning of the local spin groups with which K interacts. We calculate the heat capacity C(H), the magnetic susceptibility χ(T), and the thermodynamic curvature R. For large |J/K|, we identify four magnetic phases: ferromagnetic, antiferromagnetic, and two ferrimagnetic, according to the signs of K and J. We argue that for characterizing these phases, the strongest picture is offered by the thermodynamic geometric invariant R, proportional to the correlation length ξ. This picture has correspondences to other cases, such as fluids.
Tuning Thermoresponsive Properties of Cationic Elastin-like Polypeptides by Varying Counterions and Side-Chains.

Science.gov (United States)

Petitdemange, Rosine; Garanger, Elisabeth; Bataille, Laure; Bathany, Katell; Garbay, Bertrand; Deming, Timothy J; Lecommandoux, Sébastien

2017-05-17

We report the synthesis of methionine-containing recombinant elastin-like polypeptides (ELPs) of different lengths that contain periodically spaced methionine residues. These ELPs were chemoselectively alkylated at all methionine residues to give polycationic derivatives. Some of these samples were found to possess solubility transitions in water, where the temperature of these transitions varied with ELP concentration, nature of the methionine alkylating group, and nature of the sulfonium counterions. These studies show that introduction and controlled spacing of methionine sulfonium residues into ELPs can be used as a means both to tune their solubility transition temperatures in water using a variety of different parameters and to introduce new side-chain functionality.
Multivariate optimization of ILC parameters

International Nuclear Information System (INIS)

Bazarov, I.V.; Padamsee, H.S.

2005-01-01

We present results of multiobjective optimization of the International Linear Collider (ILC) which seeks to maximize luminosity at each given total cost of the linac (capital and operating costs of cryomodules, refrigeration and RF). Evolutionary algorithms allow quick exploration of optimal sets of parameters in a complicated system such as ILC in the presence of realistic constraints as well as investigation of various what-if scenarios in potential performance. Among the parameters we varied there were accelerating gradient and Q of the cavities (in a coupled manner following a realistic Q vs. E curve), the number of particles per bunch, the bunch length, number of bunches in the train, etc. We find an optimum which decreases (relative to TESLA TDR baseline) the total linac cost by 22%, capital cost by 25% at the same luminosity of 3 x 10 38 m -2 s -1 . For this optimum the gradient is 35 MV/m, the final spot size is 3.6 nm, and the beam power is 15.9 MV/m. Changing the luminosity by 10 38 m -2 s -1 results in 10% change in the total linac cost and 4% in the capital cost. We have also explored the optimal fronts of luminosity vs. cost for several other scenarios using the same approach. (orig.)
Structural Parameters of Star Clusters: Signal to Noise Effects

Directory of Open Access Journals (Sweden)

Narbutis D.

2015-09-01

Full Text Available We study the impact of photometric signal to noise on the accuracy of derived structural parameters of unresolved star clusters using MCMC model fitting techniques. Star cluster images were simulated as a smooth surface brightness distribution following a King profile convolved with a point spread function. The simulation grid was constructed by varying the levels of sky background and adjusting the cluster’s flux to a specified signal to noise. Poisson noise was introduced to a set of cluster images with the same input parameters at each node of the grid. Model fitting was performed using “emcee” algorithm. The presented posterior distributions of the parameters illustrate their uncertainty and degeneracies as a function of signal to noise. By defining the photometric aperture containing 80% of the cluster’s flux, we find that in all realistic sky background level conditions a signal to noise ratio of ~50 is necessary to constrain the cluster’s half-light radius to an accuracy better than ~20%. The presented technique can be applied to synthetic images simulating various observations of extragalactic star clusters.
Permanently split capacitor motor-study of the design parameters

Science.gov (United States)

Sarac, Vasilija; Stefanov, Goce

2017-09-01

Paper analyzes the influence of various design parameters on torque of permanently split capacitor motor. Motor analytical model is derived and it is used for calculating the performance characteristics of basic motor model. The acquired analytical model is applied in optimization software that uses genetic algorithms (GA) as an optimization method. Optimized motor model with increased torque is derived by varying three motor parameters in GA program: winding turns ratio, average air gap flux density and motor stack length. Increase of torque has been achieved for nominal operation but also at motor starting. Accuracy of the derived models is verified by Simulink. The acquired values of several motor parameters from transient characteristics of Simulink models are compared with the corresponding values obtained from analytical models of both motors, basic and optimized. Numerical analysis, based on finite element method (FEM), is also performed for both motor models. As a result of the FEM analysis, magnetic flux density in motor cross-section is calculated and adequate conclusions are derived in relation to core saturation and air gap flux density in both motor models.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.