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The estimations of amplitude and phase are of great importance in the synchronous phasor measurement for power systems. In addition to the off-nominal frequency, the amplitude and phase estimations accuracy level of the power system signal is affected by the inevitable noise. In this article, the effect of white noise on amplitude and phase estimations provided by DFT-based frequency shifting and filtering algorithm (FSF) is analyzed. With the help of the equivalent weighting filter’s merits, i.e., equivalent noise bandwidth and overlap correlation, the FSF-based amplitude and phase variance expressions are derived theoretically by considering the overlap of two processed samples. The effectiveness of the proposed amplitude and phase variance expressions are verified through computer simulations.

The noise at the driver seat of an agricultural tractor is produced mostly by the engine. Its characteristic broadband noise spectrum varies considerably with engine workload. The passive noise control techniques adopted in tractor cabins, based on the application of sound-absorbing and sound-proofing materials, are effective against medium-high frequencies noise components. The residual noise in sound-proof cabins is characterized by tonal emissions with low frequency components (< 500 Hz) but regarded as responsible for various disorders and diseases following long-term exposure. In addition to the "A" weighting filter adopted to evaluate occupational exposure to noise, other approaches are reported in the scientific literature considered more appropriate to evaluate low frequency noise (LFN), as well as studies testifying the effectiveness of active noise control (ANC) technologies in the low frequency range. In this article, the performance of an ANC system is evaluated in its ability to reduce noise levels inside the soundproof cabin of an agricultural tractor. To test this system, spectro-phonometric measurements of the equivalent linear sound pressure level were conducted under controlled and repeatable engine workloads, obtained by connecting the tractor to a dynamometric brake, while simultaneously acquiring the related engine performance curves. Altogether, three different couples of loudspeakers were tested. Frequency analysis in one-third octave band showed that the ANC system was mainly effective against LFN components (below 120 Hz) with peaks of reduction up to 20 dB. Then, on the basis of indications from previous studies, the data of linear sound pressure levels were processed applying the "A", "B", and "C" weighting filters, to show the different emphasis given to the effects of the system. Eventually, a point-by-point composition of the equivalent levels of sound pressure was drawn over the whole range of the engine, to highlight the conditions in which the ANC system was more effective.

Different forms of phase noise in SAR interferograms hamper the accuracy and reliability of interferometric results (InSAR and DInSAR) for the reconstruction of altimetric information and its variations. Geometric and temporal decorrelations, changes in the properties of observed surfaces and delays introduced by the atmospheric layers are the main sources of interferometric noise. In order to reduce their effects and thus increase the robustness of the phase unwrapping algorithms, filtering methods are applied at different levels of the InSAR and DInSAR processes. In this article, we propose an interferometric filtering process that combines wavelet decomposition into subbands and nonlinear weighting filter called spectral contextual filter (SCF). SCF is based on the Goldstein algorithm, whose nonlinear filter coefficients are calculated, for each element of window according to the filter's punctual parameter. This parameter is determined by estimating the adaptive coherent pseudocorrelation of overlapping blocks of the interferometric phase which combines coherence and pseudocorrelation that are both weighted by the coefficient of variation of each block. In order to show the efficiency and robustness of this process, we compared it with the subspaces decomposition filtering process that we implemented. An assessment scheme was led on the basis of a compromise between residues reduction and fringe boundaries conservation. This assessment was carried relative to the global and local parameters. The tests were conducted on complex interferograms acquired on regions with high topographical variations.

A psychoacoustic active noise control (ANC) system based on empirical mode decomposition (EMD) is proposed and implemented to improve the noise reduction performance of the control system. The noise source signal is decomposed by EMD, and the psychoacoustic parameter “loudness” of each intrinsic mode function (IMF) is initially calculated in such a system. Thereafter, the high-pass psychoacoustic weighting filter used to shape the error and reference signals is designed adaptively and automatically according to the loudness, peak frequency, and amplitude of each IMF. Three different ANC systems are simulated, and the sound pressure levels and loudness of their residual error signals are compared. The results demonstrate that the filter designed using this method can restrain the components of noise sources with small loudness better than the A-weighting shaping filter, so that the proposed control system can improve the noise reduction compared to those of the filtered-x least mean square and A-weighting shaping filters. Finally, the computational complexity of the three ANC systems is analyzed and compared. © 2019 Institute of Noise Control Engineering.

© 1989-2012 IEEE. Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the top 10 algorithms in the data mining and machine learning community. Of numerous approaches to alleviating its conditional independence assumption, feature weighting has placed more emphasis on highly predictive features than those that are less predictive. In this paper, we argue that for NB highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for NB. In CFW, the weight for a feature is a sigmoid transformation of the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model. Besides, we apply CFW to text classification and have achieved remarkable improvements.

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

Due to its simplicity, efficiency and efficacy, naive Bayes (NB) continues to be one of the top 10 data mining algorithms. A mass of improved approaches to NB have been proposed to weaken its condi...

The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is introduced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some experimental results show that super-resolution depth image can be reconstructed well by the process of the non-local filter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods.

Classical and advanced hybridization methods are employed in the control literature to calibrate the tuning parameters of internal model control (IMC) and proportional-integral-derivative (PID) controllers. This paper presents an alternative tuning design for the generalized predictive controller (GPC) that is based on both the positional process model and cost function. The proposed method involves the selection of an integral polynomial weighting filter for reference and output signals to deal with reference tracking, disturbance rejection, model-plant mismatch (MPM), minimum control energy and closed-loop robustness. The filter positional GPC (FP-GPC) obtained is transformed into a two-degree-of-freedom polynomial filtered RST structure and then into filtered internal model control (F-IMC) and PID controllers. Also, a multi-objective optimization based on genetic algorithm is applied to tune the filter parameters. Numerical and experimental essays show the effectiveness of the proposed control methodology.

The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.

Single-channel speech enhancement with deep neural networks (DNNs) has shown promising performance and is thus intensively being studied. In this paper, instead of applying the mean squared error (MSE) as the loss function during DNN training for speech enhancement, we design a perceptual weighting filter loss motivated by the weighting filter as it is employed in analysis-by-synthesis speech coding, e.g., in code-excited linear prediction (CELP). The experimental results show that the proposed simple loss function improves the speech enhancement performance compared to a reference DNN with MSE loss in terms of perceptual quality and noise attenuation. The proposed loss function can be advantageously applied to an existing DNN-based speech enhancement system, without modification of the DNN topology for speech enhancement. The source code for the proposed approach is made available.

Noise is a very important phenomena for passenger comfort in a vehicle. Engine of the vehicles produce disturbing noises in different frequencies. In the application of acoustic, there are different filters to predict the sound pressure level and disturbing noises. The aim of the present study is to investigate the noise characteristic of a compression ignition engine at different weighting filters. In the experiments, the engine was fuelled with low sulphur diesel and sunflower biodiesel fuels and it run at different engine speeds ranging from 1200 to 2400 rpm, with steps of 300 rpm. The results indicated that noise characteristic of compression engine with engine speed shows different trends at A- weighting filter than C- and Z- weighting filters especially at 1500 rpm. Moreover, in the study, it was cleared that the SPL decrease with the addition of biodiesel fuel regardless of its ratio.

The phase estimation accuracy level of voltage and current signals of the power network is affected by the inevitable noise. In this paper, the effect of the white noise on phase estimation provided by frequency shifting and filtering (FSF) is analyzed. With the help of the Equivalent Weighting Filter's merits, viz., Equivalent Noise BandWidth and Overlap Correlation, the FSF-based phase variance expression is derived theoretically by considering the overlap of two processed samples. The effectiveness of the proposed phase variance expression is verified through computer simulations.

A small angle x-ray scattering simulator has been developed for metrology and inspection applications based on the first Born approximation considering non-ideal equipment-related factors. The simulator shows good reproducibility in various device structures and measurement environments, including slit-configurations and incident angles. In addition, a Q-space adaptive weighting method is proposed to enhance about 300% accuracy, especially on non-single repeated pitch structures such as VNAND with complex x-ray diffraction patterns.

Few study gives guidance to design weighting filters according to the frequency weighting factors, and the additional evaluation method of automotive ride comfort is not made good use of in some countries. Based on the regularities of the weighting factors, a method is proposed and the vertical and horizontal weighting filters are developed. The whole frequency range is divided several times into two parts with respective regularity. For each division, a parallel filter constituted by a low- and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors. The cascading of these parallel filters obtains entire factors. These filters own a high order. But, low order filters are preferred in some applications. The bilinear transformation method and the least P-norm optimal infinite impulse response(IIR) filter design method are employed to develop low order filters to approximate the weightings in the standard. In addition, with the window method, the linear phase finite impulse response(FIR) filter is designed to keep the signal from distorting and to obtain the staircase weighting. For the same case, the traditional method produces 0.330 7 m • s–2 weighted root mean square(r.m.s.) acceleration and the filtering method gives 0.311 9 m • s–2 r.m.s. The fourth order filter for approximation of vertical weighting obtains 0.313 9 m • s–2 r.m.s. Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1, respectively. This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation, and these developed weighting filters are effective.

In power quality monitoring, voltage fluctuation is one of the power quality problems, which cause light flickers. To determine the flicker severity, the flicker meter concept was developed in an IEC 61000-4-15 standard. Generally, voltage fluctuations are described as an amplitude modulation(AM). The flicker meter of IEC 61000-4-15 as an international standard for flicker measurement recommends square demodulation method to detect flicker signals from voltage fluctuation signals. This paper suggests a new effective method using filter bank to detect and estimate flicker signals, which do not need square demodulation. For the accurate detection of flicker signals, the filter bank is designed with a full consideration of the spectrum characteristics of voltage fluctuation signals described as AM. The frequency and magnitude of the detected flicker signals are estimated using recursive method. Computer simulations were performed on synthesized signals to prove validity of the proposed method.

Bayesian network classifiers remain of great interest in recent years, among which semi-naive Bayesian classifiers which utilize superparent one-dependence estimators (SPODEs) have shown superior predictive power. Linear weighting schemes are effective and efficient ones for linearly combining SPODEs, whereas it is a challenging task for averaged one-dependence estimators (AODE) to find globally optimal and fixed weights for its SPODE members. The joint probability distribution of SPODE may not always fit different test instances to the same extent, thus a flexible rather than rigid weighting scheme would be a feasible solution for the final AODE to approximate the true joint probability distribution. Based on this promise, we propose a novel instance-based weighting filter, which can flexibly assign discriminative weights to each single SPODE for different test instances. Meanwhile, the weight considers not only the mutual dependence between the superparent and class variable, but also the conditional dependence between the superparent and non-superparent attributes. Experimental comparison on 30 publicly available datasets shows that SPODE with instance-based weighting filter outperforms state-of-the-art BNCs with and without weighting methods in terms of zero–one loss, bias and variance with minimal additional computation.

Harmonic estimation plays an important part in harmonic suppression of power system. Due to the normal fluctuation of power frequency, it is difficult to realize synchronous sampling. Consequently, the unavoidable productions, i.e., spectral leakage and picket fence effect, will affect the accuracy of harmonic analysis significantly when using the Fourier transform. To overcome this problem, a novel algorithm for power system harmonic estimation called frequency shifting and filtering (FSF) algorithm is proposed in this paper. A reference signal is at first generated to shift the frequency of the sampled signal. Then the iterative averaging filter is adopted to eliminate the spectral interferences. Finally, accurate estimation of harmonics can be achieved because only interested components are retained. Furthermore, a simplified FSF with much less computational burden is presented by using an equivalent weighting filter. The salient features of the proposed algorithm are validated by the simulations and practical experiments.

The purpose of this paper is to present an alternative synthesis for the Generalized Predictive Controller (GPC) applied to a multiple-input multiple-output (MIMO) model, using both a positional plant model and cost function to deal with reference tracking, disturbance rejection and model-plant mismatch (MPM) with minimum control energy and closed-loop robustness. This new control design called Multivariable Filtered Positional GPC (MFP-GPC) uses an integral polynomial weighting filter for the setpoint and output of the plant, thereby extending the applicability of the predictive controllers. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, and this implementation involves the filter tuning parameters using a multi-objective optimization algorithm. Numerical simulations for a hydroelectric power and a methanol/water distillation plants show the effectiveness of the proposed control methodology.

This paper proposes a new haze-removal scheme for automotive applications. In contrast that the previous works focus on enhancing the quality of haze-free images, the proposed algorithm sharpens the edges of lanes by adopting the weighting filter. Moreover, the previous internal steps, which cannot improve the sharpness of lanes, are removed to reduce the computing overheads. As a result, the proposed haze-removal algorithm improves the sharpness of lanes remarkably, while providing the real-time image processing for advanced driver assistance systems.

In this paper, we present the synthesis of a robust controller for uncertain discrete systems. The synthesis method of such a robust controller is the generalization of the Loop Shaping Design Procedure (LSDP) approach of McFarlane and Glover in the discrete case based on the work of Gu et al. We exploit the bilinear transform known as Tustin’s method in order to formulate the discrete loop shaping technique. A discrete weighting filter and a shaped discrete plant result from this technique. By taking into account the coprime factor uncertainty representation for the resulting shaped plant and by applying the small gain theorem, we define the concept of the robust stabilization of the discrete LSDP approach. This concept is based on the resolution of an optimization problem characterized by the maximum stability margin for the synthesis of the robust controller. To calculate the robust controller we transform this problem to a standard robust [Formula: see text] controller design based on the resolution of the Riccati equations. Also, we present the gap metric theory to characterize the controller’s robustness. We note that the resulting final controller is the combination of the discrete weighting filter and the robust controller. We propose then to exploit the recent work of Bouzrara et al. in order to develop a reduced robust controller by expanding the final controller on two independent Laguerre orthonormal bases. The discrete LSDP and the reduced controller approaches were validated on a Continuous Stirred Tank Reactor chemical reactor for a set of different equilibrium points in order to take into account the nonlinearities.

The purpose of this paper is to derive an alternative tuning design from Generalized Predictive Controller (GPC) that is based on both positional plant model and cost function, involving the selection of an integral polynomial weighting filter. The obtained Filter Positional GPC can be transformed into RST structure and next to Filtered Internal Model and PID controllers. Numerical and experimental essays show the effectiveness of the control methodologies.

The real-time mapping of street atmospheric pollution concentration does play an important role because its knowledge is crucial for strategy-makers to make more effective control strategies to decrease urban atmospheric pollution and improving urban atmospheric environment. Combining the conventional methods (e.g. the dispersion model prediction and neural network prediction) and mobile measurement technology (e.g. the GMAP vehicle) which their characteristics are complementary, a linear model is proposed and then a fusion approach called weighting filter derived from the concept of Kalman filter. Moreover, a self-tuning regulator is introduced to adjust the parameters of filter for the changing noise statistical characteristics over time which mainly caused by season switch. The performances of asymptotic stability and asymptotic optimality are both mathematically proven. Finally a simulation test is conducted to verify this approach.

Our image detail enhancement method can filter random noise by a gradient reciprocal weighting filter, and enhance image details adaptively by bilateral filter. The experiments indicate it can improve the detail contrast and NETD performance.