Real-time Filtering Research Articles

Spatial Decomposition-Based Fault Detection Framework for Parabolic-Distributed Parameter Processes.

Fault detection for distributed parameter processes (heat processes, fluid processes, etc.) is vital for safe and efficient operation. On one hand, the existing data-driven methods neglect the evolution dynamics of the processes and cannot guarantee that they work for highly dynamic or transient processes; on the other hand, model-based methods reported so far are mostly based on the backstepping technique, which does not possess enough redundancy for fault detection since only the boundary measurement is considered. Motivated by these considerations, we intend to investigate the robust fault detection problem for distributed parameter processes in a model-based perspective covering both boundary and in-domain measurement cases. A real-time fault detection filter (FDF) is presented, which gets rid of a large amount of data collection and offline training procedures. Rigorous theoretic analysis is presented for guiding the parameters selection and threshold computation. A time-varying threshold is designed such that the false alarm in the transient stage can be avoided. Successful application results on a hot strip mill cooling system demonstrate the potential for real industrial applications.

Real-Time Fast Fourier Transform-Based Notch Filter for Single-Frequency Noise Cancellation: Application to Electrocardiogram Signal Denoising.

Despite the considerable improvement of the common-mode rejection ratio of digital filtering techniques, the electrocardiogram (ECG) traces recorded by commercialized devices are still contaminated by residual power line interference (PLI). In this study, we address this issue by proposing a novel real-time filter adapted to single-frequency noise cancellation and automatic power line frequency detection. The filtering process is principally based on a point-by-point fast Fourier transform and a judicious choice of the analysis window length. Intensive experiments conducted on real and synthetic signals have shown that our filtering method offers very clean ECGs, due to the suppression of spikes corresponding to the PLI and the preservation of spikes outside the filter band. In addition, this method is characterized by its low computational complexity which makes it suitable for real-time cleaning of ECG signals and thus can serve for more accurate diagnosis in computer-based automated cardiac system.

A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation

In this paper, a real-time denoising filter based on modelling of stable hybrid models is presented. The hybrid models are composed of the shearlet filter and the adaptive Wiener filter in different forms. The optimization of various models is accomplished by the genetic algorithm. Next, regarding the significant relationship between Optimal models and input images, changing the structure of Optimal models for image denoising is modelled by the ANFIS. The eight hundred digital images are used as train images. For eight hundred training images, Sixty seven models are found. For integrated evaluation, the amounts of image attributes such as Peak Signal to Noise Ratio, Signal to Noise Ratio, Structural Similarity Index, Mean Absolute Error and Image Quality Assessment are evaluated by the Fuzzy deduction system. Finally, for the features of a sample noisy image as test data, the proposed denoising model of ANFIS is compared with wavelet filter in 2 and 4 level , Fast bilateral filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, run time of proposed method are evaluated. Experiments show that the proposed method has better performance than others.

Robust Localization in Map Changing Environments Based on Hierarchical Approach of Sliding Window Optimization and Filtering

Precise localization in map-changing environments is a major challenge faced in the case of autonomous vehicles. In such environments, map-matching-based localization can result in an incorrect vehicle pose because the High-Definition map (HD map) and the sensor measurements from the real environments are different. In order to solve this problem, this paper proposes robust localization in map-changing environments based on a hierarchical approach of sliding window optimization and filtering. The changing environments are explicitly modeled by a sub-map from the sliding-window-based graph optimization, and the generated sub-map is used for the map matching of the real-time filter. Since the optimized sub-map includes sensor measurement from both the changed and unchanged regions, it reduces the proportion of the changed region in the total matching region, thereby increasing the robustness of the map matching. The proposed hierarchical localization algorithm is verified and evaluated via simulation and experiments. The results show that the proposed algorithm provides a robust vehicle pose in map-changing environments.

Simulation and Experimental Research on the Charged Characteristics of Particulate Matter in the Sensor under Different Exhaust States.

The particulate matter sensor needs to be used in order to detect the concentration of particulate matter in diesel engine exhaust, monitor the working condition of diesel particulate filter (DPF) in real time, and ensure the reliable operation of DPF. The flow field and electric field of the sensor are studied and their distribution in the sensor is analyzed. At the same time, the particle tracking model was used to simulate the charged characteristics of particles in the sensor under different exhaust states. It is found that the exhaust gas flow rate maintains stability after entering the outer protection zone and concentration test zone. The electric field is a non-uniform electric field and the direction of electric field intensity is from the high voltage electrode to the grounding electrode. The electric charge per particle will decrease with the increase of exhaust flow rate, but the electric charge shows a slow growth trend. The charge of particles increases with the increase of exhaust temperature, exhaust gas concentration and particle size. The study of the charged characteristics under different environmental conditions provides a theoretical basis for further improving the prototype mechanism of a leakage flow particle sensor.

Adaptive extended Kalman filtering strategies for spacecraft formation relative navigation

The relative navigation problem for spacecraft formation flying missions in near-Earth orbit is addressed here through the design of two unique adaptive extended Kalman filter algorithms. The adaptive filters are capable of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit scenarios, including elliptical orbits subjected to perturbations. The first adaptive Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws, which are then improved through the novel inclusion of an intrinsic smoothing routine. The second approach uses an embedded fuzzy logic system based on a covariance-matching analysis of the filter residuals, where the fuzzy system has been specifically designed for the spacecraft navigation problem at hand. Numerical simulations of two spacecraft formations demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter initialization errors, dynamics modelling deficiencies, and measurement noises than the standard Kalman filter.

Real-time measurement of methane in a turbulent atmosphere over kilometer long paths with denoising methods

A wavelength modulation spectroscopy (WMS) setup for long-range methane measurements in the atmosphere over distances of kilometers was developed. To overcome the influence on the signal from noise, random factors and air turbulence, we applied denoising processing methods, which were analyzed and compared. The real-time digital Kalman filter was selected and utilized, improving the detection limit for methane sensing from 60 ppb with integration time of 60 s to 2 ppb with integration time of 1 s. To our knowledge, this is the first realization of such highly sensitive and yet simple sensor by combining the real-time filter and WMS. This approach opens the possibility to replace much more complicated instruments in sensitive measurements of trace gases when evaluating the experimental data with the Kalman filter.

An intelligent method for controlling the ECP braking system of a heavy-haul train

Abstract An intelligent control method is proposed for the electronically controlled pneumatic (ECP) braking system of a heavy-haul train. The gradient of the ramp is estimated using a Kalman filter in real time and the braking force is adjusted by the backstepping method, so that vehicles on different gradients change their brake pressure with the change in the gradient to keep the instantaneous deceleration of each vehicle consistent, thus reducing the longitudinal impact between vehicles. The longitudinal impact of heavy-haul trains with and without intelligent control under different gradients, initial braking speeds, loads and complex terrains are simulated and analysed. The results show that when the intelligent control is applied, each vehicle can adjust its brake pressure in time according to the change of gradient. The intelligent control method can effectively alleviate the longitudinal impact of vehicles, and the reduction rate remains greater than 40% under the influence of various factors.

Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation

Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation

Evidence accumulation during perceptual decision-making is sensitive to the dynamics of attentional selection

The ability to select and combine multiple sensory inputs in support of accurate decisions is a hallmark of adaptive behaviour. Attentional selection is often needed to prioritize task-relevant stimuli relative to irrelevant, potentially distracting stimuli. As most studies of perceptual decision-making to date have made use of task-relevant stimuli only, relatively little is known about how attention modulates decision making. To address this issue, we developed a novel ‘integrated’ decision-making task, in which participants judged the average direction of successive target motion signals while ignoring concurrent and spatially overlapping distractor motion signals. In two experiments that varied the role of attentional selection, we used regression to quantify the influence of target and distractor stimuli on behaviour. Using electroencephalography, we characterised the neural correlates of decision making, attentional selection and feature-specific responses to target and distractor signals. While targets strongly influenced perceptual decisions and associated neural activity, we also found that concurrent and spatially coincident distractors exerted a measurable bias on both behaviour and brain activity. Our findings suggest that attention operates as a real-time but imperfect filter during perceptual decision-making by dynamically modulating the contributions of task-relevant and irrelevant sensory inputs.

Improved parameters identification and state of charge estimation for lithium-ion battery with real-time optimal forgetting factor

Accurate estimation of state of charge (SOC) is extremely essential for energy management of electric vehicles, and precise identification of model parameters will directly affect the results of SOC estimation. However, the traditional recursive least squares (RLS) method cannot accurately track the changes of model parameters in actual complex conditions. To solve these problems, a new parameters identification method combining real-time variable forgetting factor recursive least squares (VFFRLS) and adaptive extended Kalman filter (AEKF) is proposed, and the unscented Kalman filter (UKF) method is used to calculate SOC in real time. Through comparative verification and analysis, this method owns good accuracy of model parameters identification and robustness in three commonly used equivalent circuit models. Finally, experiments under dynamic stress test (DST) cycles show that the root mean square error of terminal voltage and SOC are 0.19% and 0.07% respectively in dual polarization model with VFFRLS, which proves that the proposed method can significantly improve the estimation accuracy of SOC.

An improved post-processing technique for automatic precipitation gauge time series

Abstract. The unconditioned data retrieved from accumulating automated weighing precipitation gauges are inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: (1) the operational 15 min filter (O15), (2) the neutral aggregating filter (NAF), (3) the supervised neutral aggregating filter (NAF-S), and (4) the segmented neutral aggregating filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation primarily focuses on cold-season (October to April) accumulating automated weighing precipitation gauge data at 1 min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift and raw (minimally processed) data from several WMO Solid Precipitation Intercomparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99 %, 100 %, and 102 % of the control total precipitation for low-, medium-, and high-noise scenarios respectively for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter.

DEVELOPMENT AND IMPLEMENTATION OF TWO-DIMENSIONAL FAULT-TOLERANT PROGRAMMABLE LOGICAL CONTROLLERS

It has been revealed that a significant part of industrial control systems designed for technological facilities with more than one input and one output and the most widespread are objects with two inputs and two outputs.The article describes the programming environment for fault-tolerant two-dimensional control systems using PID controller, which software is implemented in an environment for programmable logic controllers (PLC).An experimental testing system has been implemented and software settings and regulator settings have been considered. Real-time PID controllers and filters have been implemented. It has been implemented a variation of the control system software with object imitation.

Emissions from the Open Laboratory Combustion of Cheatgrass (Bromus Tectorum)

Cheatgrass (Bromus Tectorum) is a highly invasive species in the Great Basin of the Western USA that is increasing the frequency and intensity of wildland fires. Though cheatgrass plays a significant role in the fire ecology of the Great Basin, very little is known about its combustion emissions. The fresh smoke from 16 open laboratory burns of cheatgrass was analyzed using real-time measurements and filter analysis. We presented measured intensive optical properties of the emitted smoke, including absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), single scattering albedo (SSA), and other combustion properties, such as modified combustion efficiency (MCE) and fuel-based emission factors (EFs). In addition, we gave a detailed chemical analysis of polar organic species in cheatgrass combustion emissions. We presented EFs that showed a large variation between fuels and demonstrated that analysis of combustion emissions for specific fuels was important for studying and modeling the chemistry of biomass-burning emissions.

Real-time measurement of CO2 isotopologue ratios in exhaled breath by a hollow waveguide based mid-infrared gas sensor

A hollow waveguide (HWG) based mid-infrared gas sensor using a 2.73 µm distributed feedback (DFB) laser was developed for simultaneously measuring the concentration changes of the three isotopologues 13CO2, 12CO2, and 18OC16O in exhaled breath by direct absorption spectroscopy, and then determining the 13CO2/12CO2 isotope ratio (δ13C) and 18OC16O/12CO2 isotope ratio (δ18O). The HWG sensor showed a fast response time of 3 s. Continuous measurement of δ13C and δ18O in the standard CO2 sample with known isotopic ratios for ∼2 h was performed. Precisions of 2.20‰ and 1.98‰ for δ13C and δ18O respectively at optimal integration time of 734 s were estimated from Allan variance analysis. Accuracy of -0.49‰ and -1.20‰ for δ13C and δ18O, respectively, were obtained with comparison to the values of the reference standard. The Kalman filtering method was employed to improve the precision and accuracy of the HWG sensor while maintaining high time resolution. Precision of 5.45‰ and 4.88‰ and the accuracy of 0.21‰ and -1.13‰ for δ13C and δ18O, respectively, were obtained at the integration time of 0.54 s with the application of Kalman filtering. The concentrations of 12CO2, 13CO2 and 18OC16O in breath cycles were measured and processed by Kalman filtering in real time. The measured values of δ18O and δ13C in exhaled breath were estimated to be -21.35‰ and -33.64‰, respectively, with the integration time of 1 s. This study demonstrates the ability of the HWG sensor to obtain δ13C and δ18O values in breath samples and its potential for immediate respiratory monitoring and disease diagnosis.

Real-time filtering on parallel SIMD architectures for automated quality inspection

Surface metrology in automated quality inspection is a field, among many others, affected by noise and thus requiring filtering. In surface metrology, filtering is required to remove undesired information from data in order to extract surface features and relevant properties necessary for quality control. Moreover, filtering requires immediate results, while the product is being manufactured. This way, quick correcting actions can be directly applied to solve possible manufacturing issues. This work proposes different strategies to filter height maps in real-time acquired using laser profilers, the most widely used inspection method in industrial applications. Different models to apply the filtering operations are considered, particularly assessing different alternatives to store previous samples in memory, which are required for data filtering. FIFO, double FIFO, circular and double circular buffers are evaluated. Furthermore, CPU parallelism, SIMD instructions and cache-line friendly data structures are analyzed. The proposed methods are extremely efficient, capable of filtering laser profiles at extremely high acquisition rates. The proposed methods are designed for real-time surface metrology, but they are very likely to find potential applications in different areas. The filters are compared in terms of accuracy and speed, including other well-known filters such as the spline filter. Tests analyze execution time, including cache efficiency and filtering accuracy. Results with synthetic data and real data obtained from steel strips show excellent performance, providing accurate results at very high speeds.

Kalman Filter-Based Online Identification of the Electric Power Characteristic of Solid Oxide Fuel Cells Aiming at Maximum Power Point Tracking

High-temperature fuel cells are one of the devices currently investigated for an integration into distributed power supply grids. Such distributed grids aim at the simultaneous production of thermal energy and electricity. To maximize the efficiency of fuel cell systems, it is reasonable to track the point of maximum electric power production and to operate the system in close vicinity to this point. However, variations of gas mass flows, especially the concentration of hydrogen contained in the anode gas, as well as variations of the internal temperature distribution in the fuel cell stack module lead to the fact that the maximum power point changes in dependence of the aforementioned phenomena. Therefore, this paper first proposes a real-time capable stochastic filter approach for the local identification of the electric power characteristic of the fuel cell. Second, based on this estimate, a maximum power point tracking procedure is derived. It is based on an iteration procedure under consideration of the estimation accuracy of the stochastic filter and adjusts the fuel cell’s electric current so that optimal operating points are guaranteed. Numerical simulations, based on real measured data gathered at a test rig available at the Chair of Mechatronics at the University of Rostock, Germany, conclude this paper.

Assessing the Parameters of Elementary Components of an Electrical Quantity with a Small Number of Counts

Two problems of structural analysis of electrical quantities are considered: the suppression of spectral components and selection of orthogonal harmonic components of unknown frequency. Each procedure starts with a minimum possible number of counts of the electrical variable to be processed and, as a consequence, with a minimum number of operations. The tools of structural analysis include a multilink adaptive filter, each link of which tuned to suppress one component of the observed electrical value. Structural analysis is completed with a separate procedure of selection of orthogonal components of the fundamental frequency component, the value of which is to be determined. The tuning of the real-time multilink filter done in the form of an iteration process. At each iteration step, only one link of the filter is to be tuned.

Time-domain characterization and correction of on-chip distortion of control pulses in a quantum processor

We introduce Cryoscope, a method for sampling on-chip baseband pulses used to dynamically control qubit frequency in a quantum processor. We specifically use Cryoscope to measure the step response of the dedicated flux control lines of two-junction transmon qubits in circuit QED processors with the temporal resolution of the room-temperature arbitrary waveform generator producing the control pulses. As a first application, we iteratively improve this step response using optimized real-time digital filters to counter the linear-dynamical distortion in the control line, as needed for high-fidelity repeatable one- and two-qubit gates based on dynamical control of qubit frequency.

Effects of dispersion on characterization of multi-wavelength Q-switched fiber laser

In this paper, a real-time switchable and reconfigurable multi-wavelength Q-switched fiber laser has been experimentally carried out, which is based on a real-time configurable filter (WaveShaper) and a section of unpumped erbium–ytterbium co-doped fiber (EYDF) as a saturable absorber (SA). The proposed multi-wavelength Q-switched laser has been demonstrated to generate different combination of wavelengths at 100 GHz and 200 GHz International Telecommunications Union (ITU) grids specifications. The WaveShaper not only acts as a multi-channel filter but also introduces wavelength-dependent loss (WDL) in the laser cavity. The WDL can effectively suppress the mode competition caused by homogeneous gain broadening of EDF. The attenuation at each wavelength is adjusted to equalize optical power of different channels. The effects of dispersion on the performance of multi-wavelength Q-switched fiber laser are investigated by introducing additional dispersion from WaveShaper. No significant wavelength drift was observed after monitoring of an hour, multi-wavelength Q-switched operation is clearly demonstrated with a good stability at room temperature.