The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Markku Renfors - One of the best experts on this subject based on the ideXlab platform.
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on cic decimator variants from shifting zeros to the sparse fir cic structure
Information Sciences Signal Processing and their Applications, 2007Co-Authors: Vesa Lehtinen, Markku RenforsAbstract:The cascaded integrator-comb (CIC) filter is an efficient multiplier- free structure for decimation and interpolation. However, due to its narrow stopband notches, it is useful only when the signal of interest has a small bandwidth compared to the input and Output Sample rates. Consequently, it can only be used in the first (last) few stages of multistage decimators (interpolators). Various modifications have been proposed in order to alleviate this shortcoming. We investigate the modified CIC structure proposed by Saramaki and Ritoniemi and show that its response can be further improved without increasing the implementation complexity. This remodification results in a FIR filter nested inside a CIC decimator, a structure that is already known. We then propose the use of a sparse FIR filter in this nested structure. This results in remarkable attenuation improvements for given complexity, as shown by design examples.
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ISSPA - On CIC decimator variants - from shifting zeros to the sparse FIR-CIC structure
2007 9th International Symposium on Signal Processing and Its Applications, 2007Co-Authors: Vesa Lehtinen, Markku RenforsAbstract:The cascaded integrator-comb (CIC) filter is an efficient multiplier- free structure for decimation and interpolation. However, due to its narrow stopband notches, it is useful only when the signal of interest has a small bandwidth compared to the input and Output Sample rates. Consequently, it can only be used in the first (last) few stages of multistage decimators (interpolators). Various modifications have been proposed in order to alleviate this shortcoming. We investigate the modified CIC structure proposed by Saramaki and Ritoniemi and show that its response can be further improved without increasing the implementation complexity. This remodification results in a FIR filter nested inside a CIC decimator, a structure that is already known. We then propose the use of a sparse FIR filter in this nested structure. This results in remarkable attenuation improvements for given complexity, as shown by design examples.
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EUSIPCO - Defining the wordlength of the fractional interval in interpolation filters
2002Co-Authors: Francisco Lopez, Jussi Vesma, Markku RenforsAbstract:The fractional interval is used to determine the interval between the Output Sample and the previous input Sample in interpolation filters. In this paper, the1effects of the quantization of this parameter are studied and the wordlength is derived for different kind of interpolation filters having anti-aliasing or anti-imaging properties.
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decimation by irrational factor using cic filter and linear interpolation
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: D Babic, Jussi Vesma, Markku RenforsAbstract:This paper presents an efficient way to implement flexible multirate signal processing systems with high oversampling ratio and adjustable fractional or irrational sampling rate conversion ratio. One application area is a multi-standard communication receiver which should be adjustable for different symbol rates utilized in different systems. The proposed decimation filter consists of parallel CIC (cascaded integrator-comb) filters followed by a linear interpolation filter. The idea is to use two parallel CIC filters to calculate the two needed Sample values for linear interpolation. These Samples occur just before and after the final Output Sample. This corresponds to a system where the linear interpolation is done at the higher input sampling rate.
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ICASSP - Decimation by irrational factor using CIC filter and linear interpolation
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: D Babic, Jussi Vesma, Markku RenforsAbstract:This paper presents an efficient way to implement flexible multirate signal processing systems with high oversampling ratio and adjustable fractional or irrational sampling rate conversion ratio. One application area is a multi-standard communication receiver which should be adjustable for different symbol rates utilized in different systems. The proposed decimation filter consists of parallel CIC (cascaded integrator-comb) filters followed by a linear interpolation filter. The idea is to use two parallel CIC filters to calculate the two needed Sample values for linear interpolation. These Samples occur just before and after the final Output Sample. This corresponds to a system where the linear interpolation is done at the higher input sampling rate.
Yao Dong - One of the best experts on this subject based on the ideXlab platform.
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an improved combination approach based on adaboost algorithm for wind speed time series forecasting
Energy Conversion and Management, 2018Co-Authors: Ling Xiao, Yunxuan Dong, Yao DongAbstract:Abstract As one of promising renewable energy, wind energy has an important position in the field of energy market and it has been always used to generate electricity, thus, the high precision wind speed forecasting is important but challenging for power generation system. Many researchers have devoted their attention to establish efficient wind speed forecasting models, limited by the structure of original models and database, these models could not solve overfitting problem very well and it is rare that a single wind speed forecasting model is always best in all cases since each model has its own particular strengths and weaknesses. The combining method, composed of multiple forecasting models, is regarded as a type of outstanding approach to take advantage of strengths of each model. However, the properties of the individual forecasting models may vary over time, which leads to poorly performance of the combining method using fixed weights, thus, it is more appropriate to allow the combining weights to change according to the time-varying underlying process. This study develops a reliable combination model for wind speed forecasting based on an improved Adaboost algorithm named time-vary-forecasting-effectiveness (TW-FE-Adaboost) algorithm in order to improve the overall forecasting accuracy. In the proposed model, concept drift is firstly used to deal with wind speed time series mainly because the contributions of Samples which are varying with time, the second order forecasting effectiveness is used to measure the performance of weak learners. Then, the multi-step ahead forecasting for each site is conducted using TW-FE-Adaboost model in which the input-Output Sample pairs are determined in a reasonable way. Finally, the ultimate forecast result of wind speed is obtained by aggregating the forecast result of each weak learner. The proposed model is tested using four sites wind speed series collected in Hexi corridor from wind farms located in northwest of China. The experimental results show that the proposed model outperforms all other comparison models in this paper, which demonstrates that the proposed model has superior performances for wind speed forecasting.
Ling Xiao - One of the best experts on this subject based on the ideXlab platform.
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an improved combination approach based on adaboost algorithm for wind speed time series forecasting
Energy Conversion and Management, 2018Co-Authors: Ling Xiao, Yunxuan Dong, Yao DongAbstract:Abstract As one of promising renewable energy, wind energy has an important position in the field of energy market and it has been always used to generate electricity, thus, the high precision wind speed forecasting is important but challenging for power generation system. Many researchers have devoted their attention to establish efficient wind speed forecasting models, limited by the structure of original models and database, these models could not solve overfitting problem very well and it is rare that a single wind speed forecasting model is always best in all cases since each model has its own particular strengths and weaknesses. The combining method, composed of multiple forecasting models, is regarded as a type of outstanding approach to take advantage of strengths of each model. However, the properties of the individual forecasting models may vary over time, which leads to poorly performance of the combining method using fixed weights, thus, it is more appropriate to allow the combining weights to change according to the time-varying underlying process. This study develops a reliable combination model for wind speed forecasting based on an improved Adaboost algorithm named time-vary-forecasting-effectiveness (TW-FE-Adaboost) algorithm in order to improve the overall forecasting accuracy. In the proposed model, concept drift is firstly used to deal with wind speed time series mainly because the contributions of Samples which are varying with time, the second order forecasting effectiveness is used to measure the performance of weak learners. Then, the multi-step ahead forecasting for each site is conducted using TW-FE-Adaboost model in which the input-Output Sample pairs are determined in a reasonable way. Finally, the ultimate forecast result of wind speed is obtained by aggregating the forecast result of each weak learner. The proposed model is tested using four sites wind speed series collected in Hexi corridor from wind farms located in northwest of China. The experimental results show that the proposed model outperforms all other comparison models in this paper, which demonstrates that the proposed model has superior performances for wind speed forecasting.
Yanbing Lin - One of the best experts on this subject based on the ideXlab platform.
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Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction
Renewable Energy, 2017Co-Authors: Deyun Wang, Hongyuan Luo, Olivier Grunder, Yanbing LinAbstract:Accurate wind speed forecasting is crucial to reliable and secure power generation system. However, the intermittent and unstable nature of wind speed makes it very difficult to be predicted accurately. This paper proposes a novel hybrid model based on variational mode decomposition (VMD), phase space reconstruction (PSR) and wavelet neural network optimized by genetic algorithm (GAWNN) for multi-step ahead wind speed forecasting. In the proposed model, VMD is firstly applied to disassemble the original wind speed series into a number of components in order to improve the overall prediction accuracy. Then, the multi-step ahead forecasting for each component is conducted using GAWNN model in which the input-Output Sample pairs are determined by PSR technique. Finally, the ultimate forecast series of wind speed is obtained by aggregating the forecast result of each component. The proposed model is tested using two real-world wind speed series collected respectively in spring and autumn from a wind farm located in Xinjiang, China. The experimental results show that the proposed model outperforms all other comparison models including persistence method, PSR-BPNN, PSR-WNN, PSR-GAWNN and EEMD-PSR-GAWNN models adopted in this paper, which demonstrates that the proposed model has superior performances for multi-step ahead wind speed forecasting.
P. Pouliguen - One of the best experts on this subject based on the ideXlab platform.
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Controlled Stratification Based on Kriging Surrogate Model: An Algorithm for Determining Extreme Quantiles in Electromagnetic Compatibility Risk Analysis
IEEE Access, 2020Co-Authors: T. Houret, Philippe Besnier, S. Vauchamp, P. PouliguenAbstract:An electromagnetic compatibility failure is a consequence of an applied interference level being in excess of the susceptibility level of the electronic equipment under investigation. Both interference and susceptibility levels depend on various configurations of coupling paths described by sets of unknown or uncertain parameters. It is therefore convenient to describe the applied interference and the susceptibility levels as random variables. As extreme values may have a strong impact on the risk of failure, we focus in this article on the estimation of extreme values of interference level (relevant applied fields, currents or voltages) by means of a restricted set of numerical simulations. The controlled stratification method aims at reducing the variance of estimation of extreme quantile, based on a correlated simple model. We recently highlighted that a kriging surrogate model was a good candidate to provide this simple model. Combined with controlled stratification, we obtained better estimation performances than using a standalone kriging model with the same Output Sample size. In practice, this Sample size is limited due to the excessive simulation time of electromagnetic solvers. In this paper, we propose an original algorithm, which aims at checking whether the Sample size is adequate to perform an acceptable estimation or not. We first validate the algorithm using analytical models. Finally, we apply this method to estimate the 99% quantile of the total radiated power of a source located inside an open cavity with 16 uncertain inputs. In that case, the algorithm reduces the number of calls to the initial model to approximately 40% of the budget that is required using a standard Monte Carlo approach. Moreover, it provides almost 4 times more extreme Outputs. More remarkably, our proposed algorithm provides guidance for assessing the performance of quantile estimation according to the initially Sample size of the design of experiment.