Filtering Operation Research Articles

Решение задачи определения ориентации объективов бинокля с помощью инерциальных датчиков

The paper considers a binocular-shaped device, which consists of two solid bodies — lenses connected by a common axis of rotation, and introduces a solution to the problem of the absolute and relative positioning of each of the lenses of the device using accelerometers, angular velocity sensors and Hall sensors installed in each of the lenses. To solve the problem, we developed an algorithm based on the Madgwick filter. The algorithm uses data from all sensors and determines the orientation of both lenses from this data. In addition to the information received from the sensors, the solution of the problem uses information about the geometric relationship imposed on the system — the common axis of rotation of both lenses. The ARTrack video analysis system was used to verify the obtained algorithm. The results of the filter operation were verified using the records received from the video analysis system.

Разработка методики расчета перепада давления на топливном фильтре из-за появления углеродсодержащих осадков в среде жидкого углеводородного горючего (охладителя)

The paper introduces a theoretical study related to a new method for calculating the fuel filter pressure drop caused by sedimentation in a liquid hydrocarbon fuel (cooler) medium; describes some thermophysical properties of sediments in fuel systems of various heat engines; examines the inventions aimed at preventing the failure of filters and pressure drop in them. As the overview and analysis of scientific and technical literature discovered no methods for calculating the pressure drop on the filters of heat engines and power plants, we developed a new formula for calculating the fuel filter pressure drop caused by sedimentation, taking into account the thermal and electrical nature of carbon-containing sediments. Using this formula, we found a new method for calculating the pressure change, which can be used to calculate the residual life of filter elements, theoretically approbated the new method relying on the results of experimental studies by previous researchers who used aviation kerosene. As a result, we obtained the fuel filter operating time to failure in several operating modes. The method proves to be valid, as it is applicable for calculating the pressure drop in almost all ground, air, and aerospace-based heat engines and power plants under various operating conditions of the fuel system. Furthermore, the method can be used with various materials of the metal walls of filter elements, pumping speeds of hydrocarbon fuel, pressures at the fuel filters inlet, temperature conditions inside the fuel supply, and cooling channels of engines and power plants.

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4±0.3% is obtained in overall teeth segmentation. The proposed post-processing stages reduce the mean error of tooth count to 6.15%, whereas the error without post-processing is 26.81%. The performances of both segmentation and tooth counting are the highest in the literature, to our knowledge. Moreover, this is achieved by using a relatively small training dataset, which consists of 105 images. Although the aim in this study is to segment tooth instances, the presented method is applicable to similar problems in other domains, such as separating the cell instances.

Image Stripe Noise Removal Based on Compressed Sensing

The sensors or electronic components are vulnerable to interference in the camera’s imaging process, usually leading to random directional stripes. Therefore, a method of stripe noise removal based on compressed sensing is proposed. First, the measurement matrix of the image with stripe noise is established, which makes the stripe images equivalent to the observation of the original image. Second, the relationships between the corresponding coefficients of adjacent scales are defined. On this basis, the bivariate threshold function is set in the curvelet sparse domain to represent the features of images. Finally, the Landweber iteration algorithm of alternating convex projection and filtering operation is achieved. Furthermore, to accelerate the noise removal at the initial stage of iteration and preserve the image details later, the exponential threshold function is utilized. This method does not need many samples, which is different from the current deep learning method. The experimental results show that the proposed algorithm represents excellent performance in removing the stripes and preserving the texture details. In addition, the PSNR of the denoised image has been dramatically improved compared with similar algorithms.

SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state‐of‐the‐art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor‐critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data‐driven method is used to improve the poor asymptotic performance, and the model‐driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non‐convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.

Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.

Deformable Wiener Filter for Future Video Coding.

In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this shortcoming, the widely-used unsupervised parameter estimation method by non-local filters limits the performance. In view of this, we propose a deformable Wiener Filter (DWF). It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory. In the filtering process, local adjacent samples and non-local similar samples are first derived for each sample of interest. Then the to-be-filtered samples are classified into specific groups based on the patch-level noise and sample-level characteristics. Samples in each group share the same filter coefficients. After that, the local and non-local reference samples are adaptively fused based on the classification results. Finally, the filtering operation with outlier data constraints is conducted for each to-be-filtered sample. Moreover, the performance of the proposed DWF is analyzed with different reference sample derivation schemes in detail. Simulation results show that the proposed approach achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations, respectively.

ПРОЦЕССЫ ЗАГРЯЗНЕНИЯ И ОЧИСТКИ ТУРБИННОГО МАСЛА В СИСТЕМАХ СМАЗКИ ПАРОВЫХ ТУРБИН

The dependence of the parameters (cleanliness, contamination and purification) of the working fluid of the bearing lubrica-tion system and steam turbine control is obtained. Filters are intended to purify by removing solid contaminants that are harmful to the oil and the machine. If the filter is doing a good job, it is removing contaminants as fast as they are entering (ingression). This is known as mass balance. The number of particles coming into the oil system (particle ingression) equals the number being caught by the filter (particle removal); this achieves a stabilized and controlled oil cleanliness lev-el. The effect of air dispersed in oil on the operation of frame filters in steam turbine lubrication systems was analyzed. It has been established that when filtering aerated oil, soft particles (gas bubbles) increase the screening ratio of solid con-taminants by more than 4 times. Calculations show that air dispersed in oil increases the purity of the filtrate in the system by 21 times.

Research and results of mathematical modeling of block ion-exchange water treatment plants operating modes and regeneration

The problem of water treatment at thermal power plants using ion-exchange technologies is a multi-parameter task. Mathematical modeling is essential for research and optimization of ion exchange technology. The analysis of hydrodynamic processes during the operation of ion-exchange filters was carried out according to the developed mathematical model. Also, a physicochemical analysis of the composition of the water treatment plant solutions under real conditions was carried out. It is shown that in the cationite and anionite filters, the flow movement occurs mainly in a mixed hydrodynamic mode. This mode of regeneration and the filter design do not allow achieving the minimum consumption of the reagent for regeneration, the minimum volume of wastewater and the maximum output of demineralized water. The mixed mode of the anion exchange filter operation allows division of the outgoing solution flow into fractions, which can be successfully used in the TPP water cycle.

Projection-compliant database generation

Synthesizing data using declarative formalisms has been persuasively advocated in contemporary data generation frameworks. In particular, they specify operator output volumes through row-cardinality constraints. However, thus far, adherence to these volumetric constraints has been limited to the Filter and Join operators. A critical deficiency is the lack of support for the Projection operator, which is at the core of basic SQL constructs such as Distinct, Union and Group By. The technical challenge here is that cardinality unions in multi-dimensional space, and not mere summations, need to be captured in the generation process. Further, dependencies across different data subspaces need to be taken into account. We address the above lacuna by presenting PiGen , a dynamic data generator that incorporates Projection cardinality constraints in its ambit. The design is based on a projection subspace division strategy that supports the expression of constraints using optimized linear programming formulations. Further, techniques of symmetric refinement and workload decomposition are introduced to handle constraints across different projection subspaces. Finally, PiGen supports dynamic generation, where data is generated on-demand during query processing, making it amenable to Big Data environments. A detailed evaluation on workloads derived from real-world and synthetic benchmarks demonstrates that PiGen can accurately and efficiently model Projection outcomes, representing an essential step forward in customized database generation.

Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images

Speckle Reduction Anisotropic Diffusion filter which is used to despeckle ultrasound images, perform well at homogeneous region than in heterogeneous region resulting in loss of information available at the edges. Extended SRAD filter does the same, preserving better the edges in addition, compared to the existing SRAD filter. The proposed Extended SRAD filter includes the intensity of four more neighboring pixels in addition with other four that is meant for SRAD filter operation. So, a total of eight pixels are involved in determining the intensity of a single pixel. This improves despeckling performance by maintaining the information accessible at an image’s edges. The proposed filter produces better Peak Signal to Noise Ratio, Root Mean Square Error and Structural Similarity Index values for standard test images with different noise levels with variance 0.3, 0.35 and 0.4. It also performs well in denoising breast ultrasound images at different noise levels.

Design and Analysis of Compact 5th Mode Balanced Substrate Integrated Waveguide Band-Pass Filter for 39 GHz

This paper presents a balanced fifth-order Substrate Integrated Waveguide (SIW) band-pass filter operating at 39 GHz. The filter uses the balanced nature of the SIW resonators and the odd-mode property of the TE102 mode. Five rectangular resonators have been used to develop a fifth-order band-pass filter and the couplings between them with the external quality factors have been determined. The filter yields a good response in the KA band of the electromagnetic spectrum. It has a smaller footprint and operates at a higher frequency range compared to other filters with the same technology. The Common-Mode (CM) signal has been effectively suppressed to more than -30 dBs throughout the considered range. The return loss of -30 dBs and an insertion loss of -4 dBs for the Differential-Mode (DM) operation are achieved at the operating frequency of 39 GHz. The half-mode operation of the filter has been also implemented and the results have proven to be satisfactory.

СОВЕРШЕНСТВОВАНИЕ ТЕХНИЧЕСКИХ СРЕДСТВ СИСТЕМ КОНТРОЛЯ И ОХРАНЫ МОРСКОЙ ПРИРОДНОЙ СРЕДЫ ОТ ЗАГРЯЗНЕНИЯ НЕФТЬЮ

Data on environmental monitoring and the marine environment pollution by oil are presented and new highly efficient technical means of oil-containing water purification systems are developed, which have high quality of oil-containing water purification, a long service life of filter elements, simple design and operation. The combined separation system for the purification of oily waters has a high efficiency and provides the purification of oily waters to a concentration of petroleum products lower than 5 million-1. The design of the regenerated filter and separator with granular loading ensures the regeneration of filter elements without disassembling and replacing them, which significantly simplifies the maintenance and operation of the system.

Study of Ti – Al based high-temperature cermet filters

Operation in corrosive media and/or at high temperatures requires improved characteristics of the performance and durability of filtering equipment. The Ti – Al intermetallic system combines low density with high strength and exhibits high resistance to oxidation and corrosion at elevated temperatures. We present the results of studying high-temperature Ti – Al cermet products (filters) with nanoscale pores. Filters were obtained from a mixture of Ti and Al powders (mass ratio 40:60) by thermal explosion. XRD methods showed that the synthesized material consists of two main phases: TiAl3 and Al2O3. The microstructure analysis revealed the presence of large-sized TiAl3 structures and spherical Al2O3 aggregates enveloped by nanostructured TiAl3. Moreover, nanoscale fibrous TiAl3 compounds forming a multilevel developed cobweb-like structure are observed in the entire volume of the pore space of the material. The open porosity of the material was 48%, the pore size was 0.1 – 0.2 μm, the efficiency of the porous material was 99.999%, the resistance to gas flow was 100 mm of water column, and the filtration index was 0.062. Thermal tests revealed the possibility of effective use of filters under conditions of elevated (up to 800°C) temperatures and corrosive media due to the chemically stable and heat-resistant binary structure of the material. The results obtained can be used to improve the methodology of the development and operation of high-temperature cermet filters based on Ti – Al.

Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

The stochastic resonance (SR) method is commonly used in incipient fault diagnosis to extract weak fault features from complex diagnostic signals. However, the extraction effect of a SR system is highly dependent on the choice of system parameters as well as the complexity of input signals. To solve this problem, the present study proposes an adaptive parameter-induced SR method, in which high pass filter and Teager energy operator (TEO) are combined to pre-process the original signal while the grasshopper optimization algorithm is introduced to optimize the SR system parameters. The proposed method is employed to diagnose a set of experimental vibration signals of a planetary gearbox with incipient localized root crack and incipient distributed surface wear as well, leading to satisfactory diagnosis results. Comparing with the existing adaptive stochastic resonance methods, the present method claims the merits of a high signal-to-noise ratio and low computation cost.

Синтез дискретных полосовых и режекторных фильтров методом инвариантных дифференциальных уравнений.

Introduction – The issue of constructing discrete band-pass and notch filters using differential equations of continuous analog filters of low and high frequencies is considered. Purpose – Development of a technique for the synthesis of tunable band-pass and notch filters with a constant width of the amplitude-frequency characteristics, regardless of the filter tuning frequency. Setting the task – Based on the invariant differential equations of continuous analog filters of low and high frequencies, find the weight coefficients of the difference equations that determine the operation of discrete bandpass and notch filters that have a constant width of amplitude-frequency characteristics under conditions of variable filter tuning frequency. Method – The synthesis is based on the presentation of the mathematical frequency transfer function of the filter separately in the region of positive and negative frequencies, for each of which a bilinear z-transform is applied shifted to the tuning frequency of the selected region. Results – Difference equations of discrete filters are obtained from the given differential equations of continuous analog filters of low and high frequencies. Frequency-shifted bilinear z-transformations are presented. Transfer functions in the z-plane are obtained separately for the regions of the frequency transfer function of the filter located in the ranges of positive and negative frequencies. By summing the transfer functions in the z-plane for the regions of positive and negative frequencies and multiplying these transfer functions, the resulting transfer functions in the z-plane are obtained for discrete bandpass and notch filters. By passing from the z-plane to the jω, expressions for the frequency transfer functions of the synthesized band-pass and notch filters are obtained. The calculation of their amplitude-frequency characteristics has been made. It is shown that the width of the amplitude-frequency characteristics of the synthesized filters remains constant when the filter tuning frequency is changed. Practical relevance – The developed technique for the synthesis of tunable real discrete band-pass and notch filters with a constant frequency response width based on invariant differential equations of continuous analog filters will be very useful in the construction of adaptive systems and signal processing devices, such as signal detection and filtering systems, Doppler velocity meters, selection systems moving targets.

A new collaborative optimization method for a distributed energy system combining hybrid energy storage

• A distributed energy system with hybrid energy storage was proposed • For hybrid energy storage, two different system operation strategies were proposed • A new distributed energy system was optimized by a two-layer collaborative optimization method • A new distributed energy system was applied in the nearly zero-energy community • The energy balance, economy, and energy-saving, etc., of the new system, were studied The application of hybrid energy storage to distributed energy systems can significantly improve energy efficiency and reduce the investment operating cost of the system. However, inadequate efforts are found focusing on the investigation of the integration of the two systems and optimization configuration and operation strategy of systems. Therefore, a novel distributed energy system combining hybrid energy storage was proposed, and the system optimization configuration and operation strategy of the novel system were considered simultaneously. The hybrid system contained heat storage and two power storage forms, i.e., supercapacitors and lithium battery. The influence of hybrid energy storage on distributed energy systems was fully considered. Subsequently, a two-layer collaborative optimization method for the novel system considering energy efficiency, economy, and environmental protection was presented. The novel system was applied to a nearly zero-energy community. The results indicate that the primary energy-saving and pollutant equivalent emission reduction rates of the novel system are evaluated to be 54.8% and 63.6%, respectively, under the low-pass filter operation strategy combined with the secondary feedback adjustment of the supercapacitor charge state. The system configuration and operation strategy can be optimized simultaneously by the two-layer collaborative optimization method.

New methods of electricity generation using steam from waste heat boilers fired by waste gases from high-temperature plants in ferrous metallurgy

Currently, the thermal energy of the steam generated by the heat recovery steam generator (HRSG) of high-temperature ferrous metallurgy units is used practically only for space heating and hot water supply. In this regard, the consumption of heat energy is unstable during the day and over the seasons and, as a rule, much less steam is consumed than is generated. This is because HRSGs are an integral part of high-temperature installations. They provide primary dedusting and pre-cooling of waste gases, which is necessary for the operation of filters. Then these combustible gases are used as energy carriers and are sent to gas pipeline networks. As a result, steam is generally not used efficiently. The steam generated by most HRSGs is not superheated and has a high moisture content, which makes it difficult to use this steam in steam turbines. However, even in the case of superheated steam, its low volumetric flow rate makes the use of steam turbines impractical due to the low efficiency of low-power turbines. This article will discuss the possibility of using a steam-piston engine to generate electricity from steam generated by the HRSG and evaporative cooling systems. In contrast to the classic reciprocating steam engines, the steam-piston engines have a relatively high rotational speed. As a result, they are smaller and can be connected directly to modern power generators with a 1500 rpm speed. Such engines can be manufactured by converting serial internal combustion engines, which significantly reduces their cost and provides a short payback period for such equipment.