Multilayer Filters Research Articles

Defect Detection of Polyethylene Gas Pipeline Based on Convolutional Neural Networks and Image Processing

Abstract In this paper, a method based on image recognition was proposed to detect the defects of polyethylene (PE) gas pipeline, especially the deformation due to the indentation. First, the pipeline -detection VGG (PD-VGG) model was established based on the convolutional neural network (CNN), and appropriate model parameters were optimized through model training. The defect recognition rate of the improved model can reach 94.76%. Following, the weighted average graying algorithm was used to separate the defects characterized by deformation. Then, an improved gamma correction algorithm was applied to achieve image enhancement, and the interference of impurities adhered on intersurface of pipeline was also removed by using multilayer filters. The edge detection of the defect image was completed by using the Canny operator, and following the screening between the target contour and the interference contour by using top-contour. Finally, the algorithm for minimum outer rectangle algorithm was used to fit the defect contour, and the eigenvalues of deformation defects were extracted. The results indicate that the above defect detection method can better extract the deformation contour of the dented pipeline. The high agreement with the experimental results provides a basis for the research of effectively recognizing whether the pipeline has undergone ductile failure only through profile detection of defects.

All‐In‐One Photonic Crystals With Multi‐Stimuli‐Chromic, Color‐Recordable, Self‐Healable, and Adhesive Functions

AbstractFabricating photonic crystals (PCs) with dynamic multi‐stimuli‐responsive structural colors, recordable colors, and self‐healable properties is significant for their emerging applications, yet remains a big challenge. Here, an all‐in‐one multifunctional PC (MFPC) film with outstanding mechanochromic, thermochromic, solvatochromic, color/shape‐recordable, self‐healable, and adhesive functions is designed and prepared by simply non‐close‐assembling silica particles into the unique 2‐[[(Butylamino)carbonyl]oxy]ethyl acrylate (BCOEA) followed by photopolymerization. The success is mainly due to the rational combination of non‐close‐packing structures and BCOEA's characteristics, including its urethane groups with numerous sacrificial hydrogen bonds, temperature‐dependent refractive index, swellable and deformable polymer network, as well as low glass‐transition temperature. The MFPC's hue and color saturation can be reversibly and dynamically modulated by strain/pressure/solvents and temperature, respectively, thereby realizing visually and spectrally sensing these stimuli. More interestingly, the color‐responsiveness combined with other functions endows MFPCs with fascinating emerging applications, including multilayer optical filters, inkless printing, reconfigurable multicolor patterns, stretching‐based anti‐counterfeiting, etc. This work offers a new perspective for designing next‐generation smart photonic materials and will facilitate their all‐round applications.

Darwinian Philosophy as Optimization Method for Design High Reflection Mirror Include New Merit Function

In this paper we proposes the philosophy of the Darwinian selection as synthesis method called Genetic algorithm ( GA ), and include new merit function with simple form then its uses in other works for designing one of the kinds of multilayer optical filters called high reflection mirror. Here we intend to investigate solutions for many practical problems. This work appears designed high reflection mirror that have good performance with reduction the number of layers, which can enable one to controlling the errors effect of the thickness layers on the final product, where in this work we can yield such a solution in a very shorter time by controlling the length of the chromosome and optimal genetic operators . Result shows that the construction of multilayer high reflection mirror using in this approach can be considered as a master stone for design another type of filters with most complicated performance, and it is difficult designing in other approach The experiment results demonstrate that our approach is a powerful technique. It is enable to locate the global optimum optimal automatically with high confidence without need for a good starting design.

A multi-layer antimicrobial cellulose filter with electrostatic adsorption properties prepared by coating cellulose with chitosan and CuO

The health impacts of air pollution such as particulate matter (PM) and bioaerosols have prompted a focus on the utilization of personal protective filters. Traditional filters pose environmental concerns due to their non-biodegradable composition, while cellulose presents a promising alternative for biodegradable air filtration applications. This research involved the development of cellulose filter substrates with a customizable pore structure by adjusting the nanofiber content in cellulose filters and employing a simultaneous freeze-drying method. By integrating chitosan and CuO nanoparticles, the filters gained electrostatic adsorption capabilities, leading to a substantial enhancement in filtration efficiency, as well as imparting moisture resistance and durable antimicrobial properties to the filter. Subsequently, a three-layer cellulose filter integrated with chitosan and CuO (abbreviated as Int-Cs&CuO) was constructed based on the generalized Murray’s law to enable multi-stage PM filtration. The Int-Cs&CuO cellulose filter demonstrated filtration efficiencies of 99.2 % for non-oily PM0.3 and 99.5 % for oily PM0.3, with a low pressure drop of 49.5 Pa. Under humid conditions, the filtration efficiency of the Int-Cs&CuO cellulose filter against PM0.3 particles was minimally impacted, showcasing a decrease of only 1.3 %. Notably, the filter exhibited complete biodegradation in composted soil within approximately 2 months.

Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters

The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these perturbations are difficult for humans to detect, they can easily mislead deep learning classifiers. Noise injection, as a defense mechanism, can offer a provable defense against adversarial attacks by reducing sensitivity to subtle input changes. However, these methods face issues of computational complexity and limited adaptability. We propose a multilayer filter defense model, drawing inspiration from filter-based image denoising techniques. This model inserts a filtering layer after the input layer and before the convolutional layer, and incorporates noise injection techniques during the training process. This model substantially enhances the resilience of image classification systems to adversarial attacks. We also investigated the impact of various filter combinations, filter area sizes, standard deviations, and filter layers on the effectiveness of defense. The experimental results indicate that, across the MNIST, CIFAR10, and CIFAR100 datasets, the multilayer filter defense model achieves the highest average accuracy when employing a double-layer Gaussian filter (filter area size of 3×3, standard deviation of 1). We compared our method with two filter-based defense models, and the experimental results demonstrated that our method attained an average accuracy of 71.9%, effectively enhancing the robustness of the image recognition classifier against adversarial attacks. This method not only performs well on small-scale datasets but also exhibits robustness on large-scale datasets (miniImageNet) and modern models (EfficientNet and WideResNet).

Solar PV integrated simplified multilevel inverter configuration for power quality improvement using multilayer Gamma filter

Solar PV integrated simplified multilevel inverter configuration for power quality improvement using multilayer Gamma filter

Easy-to-design wide stop-band multi-layer metamaterial filter based on symmetrical modified circular resonators for terahertz communication applications and electromagnetic interference shielding

Terahertz filters represent an essential element in modern communications system engineering. Designing a stopband filter with unique electrical qualities remains a relatively complicated task to achieve. In this context, the present paper suggests an easy-to-design bandstop filter to exploit it in terahertz communications and electromagnetic interference (EMI) shielding. The proposed filter is based on the multi-layer metamaterials stacking technique; it is constituted by a five-layer metal-insulator-metal-insulator-metal (MIMIM) microstructure. The two dielectric layers in polyimide with the three metallic layers in gold represent a total thickness of 43 μm. Each metallic layer is represented by a modified metamaterial resonator (MTMR) of the same symmetrical circular shape. The filter cell dimensions are optimized as 120 × 120 μm2 to have the desired stopband feature. The electrical qualities of the proposed filter have been analyzed by the finite element method (FEM) via the ANSYS high-frequency structure simulator software (HFSS). Through the simulation outcomes, salient filter features were highlighted. A wide −20 dB bandwidth of the order of 1.062 THz centered at 1.872 THz with a low transmission rate in the stopband which is less than 0.87 %, a significant square ratio of 0.86 and a relative bandwidth of around 57 %. Moreover, the absorption rate of the stopband has reached more than 97 % with excellent flat-top characteristics. To give more credibility to our results, we have presented a parametric analysis that included several components. In addition, the designed filter was insensitive to the polarization angle thanks to the symmetrical metamaterial resonators. Based on all these results and due to the simplicity of its design combined with all these unique features, our filter can be a prominent contender in the field of THz communications. It can be also required for terahertz imaging and EMI shielding.

Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics

We propose a novel and versatile computational approach, based on hierarchical COSFIRE filters, that addresses the challenge of explainable retina and palmprint recognition for automatic person identification. Unlike traditional systems that treat these biometrics separately, our method offers a unified solution, leveraging COSFIRE filters’ trainable nature for enhanced selectivity and robustness, while exhibiting explainability and resilience to decision-based black-box adversarial attack and partial matching. COSFIRE filters are trainable, in that their selectivity can be determined with a one-shot learning step. In practice, we configure a COSFIRE filter that is selective for the mutual spatial arrangement of a set of automatically selected keypoints of each retina or palmprint reference image. A query image is then processed by all COSFIRE filters and it is classified with the reference image that was used to configure the COSFIRE filter that gives the strongest similarity score. Our approach, tested on the VARIA and RIDB retina datasets and the IITD palmprint dataset, achieved state-of-the-art results, including perfect classification for retina datasets and a 97.54% accuracy for the palmprint dataset. It proved robust in partial matching tests, achieving over 94% accuracy with 80% image visibility and over 97% with 90% visibility, demonstrating effectiveness with incomplete biometric data. Furthermore, while effectively resisting a decision-based black-box adversarial attack and impervious to imperceptible adversarial images, it is only susceptible to highly perceptible adversarial images with severe noise, which pose minimal concern as they can be easily detected through histogram analysis in preprocessing. In principle, the proposed learning-free hierarchical COSFIRE filters are applicable to any application that requires the identification of certain spatial arrangements of moderately complex features, such as bifurcations and crossovers. Moreover, the selectivity of COSFIRE filters is highly intuitive; and therefore, they provide an explainable solution.

Сравнительное изучение сохранности антоцианов в гранатовых соках-заготовках горячего розлива

Abstract. Taking into account the biological and antioxidant activity of anthocyanins and their responsiveness to various technological factors, the purpose of this work was to identify a balance between the total losses of anthocyanins and anthocyanins of sediment in experimental samples of pomegranate juice-semi-finished product. The juice of pomegranates of the Guleisha pink variety from Geokchai (Azerbaijan), obtained from their grains using a laboratory press, was used as the starting material. The freshly squeezed juices were filtered through a multilayer gauze filter, additives were introduced into some of them to stabilize the color, after which they were stabilized by hot filling (heated to 85 оC in 15 minutes), using preservatives (sorbic acid, sodium benzoate) or by instant heating and cooling in tubular heat exchangers. The juices were stored in a regular warehouse at a lower temperature limit of 5 оC and an upper temperature limit of 18 оC. Cold storage of one of the hot filling juices and juice from instant heating to 85 оC and cooling to 15-20 оC (both in 10 seconds) at a temperature of 0...+ 1 оC was also tested. Changes in anthocyanins were judged by the optical density of the tested solutions at a wavelength of 520 nm before and after treatment with hydrogen peroxide, which discolors anthocyanins and does not act on newly formed dark-colored pigments. It was found that at the end of the specified shelf life, the total color intensity of semi-finished juices increased by 6.2...10.2 % was determined by dark-colored products of the melanoidin formation process and other processes that took place without the participation of anthocyanins; the percentage of preservation of anthocyanins proper in semi-finished juices with and without various additives, such as vitamin B1, vitamin E, etc., varied from 40.31 to 60.84, and the total loss of anthocyanins was almost 100 % associated with loss they are precipitated. Keywords: pomegranate juice purchases of long-term storage, glass cylinders, hot filling, anthocyanins of settled juice, anthocyanins of sediment, dark-colored pigments

Determination of the nonlinear thermal expansion coefficient of an epoxy used as an expander and its effects over a Fabry-Perot cavity filled with a polymer

In this work, the nonlinear thermal expansion coefficient (TEC) of epoxy clay is determined by means of monitoring the changes induced in the optical thickness of a Fabry-Perot cavity filled with polymer. Here, the epoxy is used to form a long sleeve that holds all elements that form a multilayer interferometric fiber tip filter. Additionally, it is shown that this sleeve acts as an axial strain actuator to change the thickness of a polymer cavity in the order of a few microns. This allows the filter’s spectral fringe pattern to be tuned by several orders of interference, inducing considerable changes in its free spectral range (FSR). Furthermore, it is shown that this filter design has an additional advantage that makes it possible to determine either the TEC or the thermo-optic coefficient (TOC) of another layer material of the filter by using the same measured spectra. Here, as a proof of principle, experimental results of the TEC of silicon were estimated, showing that they are quite close to what was reported in the literature and, therefore, that these can be used for reference purposes. Finally, it is demonstrated that by considering the determined TEC of the epoxy, the overall filter spectrum can be very well modeled, and its main features can be explained for a wide range of temperatures.

A multi-layer membrane filter made of potassium catalyst and three-way catalyst for a passive after-treatment system

A multi-layer membrane has been fabricated to integrate a Three-Way Catalyst (TWC) and Gasoline Particulate Filter (GPF) into one device, called a four-way catalytic converter. The top layer, made of nano-scale potassium catalyst particles, traps Particulate Matter (PM) with almost 100% filtration efficiency at all times and oxidizes PM (mostly soot) with a significantly reduced temperature of 476°C at the oxidation peak. Moreover, the bottom layer catalyst is comprised of sub-micro TWC particles to combine NO reduction and CO oxidation capabilities. The effective temperature range for the simultaneous removal of all pollutants was between 420°C–500°C.

Experimental Dust Absorption Study in Automotive Engine Inlet Air Filter Materials.

The purpose of this study was to empirically evaluate the performance of fibrous materials that meet the criteria for inlet air filtration in internal combustion engines. The characteristics of filtration efficiency and accuracy, as well as the characteristics of flow resistance, were determined based on the mass of dust accumulated in the filter bed during the filtration process. Single-layer filter materials tested included cellulose, polyester, and glass microfiber. Multilayer filter media such as cellulose-polyester-nanofibers and cellulose-polyester were also examined. A new composite filter bed-consisting of polyester, glass microfiber, and cellulose-and its filtration characteristics were evaluated. Utilizing specific air filtration quality factors, it was demonstrated that the composite is characterized by high pre-filtration efficiency (99.98%), a short pre-filtration period (qs = 4.21%), high accuracy (dpmax = 1.5-3 µm) for the entire lifespan of the filter, and a 60-250% higher dust absorption coefficient compared to the other tested materials. A filtration composite bed constructed from a group of materials with different filtration parameters can be, due to its high filtration efficiency, accuracy, and dust absorption, an excellent filter material for engine intake air. The composite's filtration parameters will depend on the type of filter layers and their order relative to the aerosol flow. This paper presents a methodology for the selection and testing of various filter materials.

KGCF: Social relationship-aware graph collaborative filtering for recommendation

With the increasing popularity of recommendation techniques and social networks, social network recommendation has become a significant research field, i.e., predicting a user's preferences based on her or his historical interaction data, because the social relationships of users can not only enrich their interaction information, but also imply the accurate characterization of their preferences. Although there are some existing studies that predict users' preferences by using the characteristics of social relationships, their time complexity and hardware resource consumption are often very expensive, which limits the feasibility in large-scale real-world scenarios. To address these issues, in this paper, we propose a simple and effective K-core Graph Collaborative Filtering (KGCF) model to incorporate the user's social features into a recommendation framework. The user's interaction information is auto-encoded linearly by incorporating the social relationship graph into the user's interaction information and constructing a multi-layer graph filter. The linear autoencoder graph filter alleviates the highly sparse data problem and dramatically reduces the training time and hardware resource consumption. Experimental results on several real-world datasets show the superior effectiveness and spatiotemporal efficiency compared with the existing baselines, especially the training speed is significantly accelerated for large-scale datasets.

Application of a hybrid-driven framework based on sensor optimization placement for the thermal error prediction of the spindle-bearing system

The precise thermal error prediction of spindle-bearing systems (SBSs) necessitates a comprehensive analysis of information gathered from multi-source sensors. However, limited data availability due to structural constraints poses challenges to fully characterize the system state. In this study, we introduce a data-model hybrid-driven framework based on sensor optimization placement for accurate thermal error prediction of SBSs. Firstly, a thermal hypernetwork method is developed to consider uneven temperature distribution and establish a unified information fusion model for state estimation. Secondly, based on an analysis of the rapidity and robustness, robust geodesic distance-based fuzzy c-medoid clustering with a simulated annealing algorithm (RGDFCMSA) is proposed to optimize sensor placement by minimizing the information entropy of the system. Next, uncertain parameters with estimability are selected based on SIAN and Sobol’s sensitivity indicator under optimal sensor placement. Furthermore, a multilayer particle filter (MLPF) is proposed to estimate temperature fields and predict the thermal error of SBSs by fusing information from multiple sources with different fidelity. Finally, experiments under different working conditions are conducted to validate the effectiveness and accuracy of the proposed method. The result indicates that the proposed framework is capable of an accurate estimation of the global temperature field, uncertain thermal parameters and thermal errors.

Design and preparation of L-band multilayer low-group delay filter based on inner sputtering

Article propose a design for an L-band multi-layer MEMS cross-finger filter based on pore interior wall sputtering. Article analyze the structural parameters, such as resonator thickness and TSV structure, using HFSS simulation software. Findings indicate that the filter has a central frequency of 1.5 GHz, a bandwidth of 140 MHz, a band loss of less than 1 dB, return loss is greater than 25 dB, and a band group delay of less than 800 ps. Furthermore, Article conduct process design optimization and finished product testing for the filter, including optimizing the TSV process for the hole inner wall sputtering process. These results demonstrate the filter's broad application prospects.

Multilayer circular SIW cavity differential bandpass filter with wide‐stopband characteristics

AbstractIn this paper, a compact differential bandpass filter with wide‐stopband is presented. The proposed differential filter is designed using two circular substrate‐integrated waveguide (SIW) cavities coupled through a pair of symmetric coupling slots. The filter operates in TM110 mode of the circular SIW cavity. The degenerate TM110 mode is shifted to higher frequency by the introduction of two pairs of perturbation vias. The center frequency of the differential passband can be tuned by changing different design parameters such as location of perturbation vias and length of the slotlines. As a proof of the proposed concept, a second‐order differential bandpass filter is designed, fabricated, and measured. The measured results are in good agreement with the electromagnetic‐simulated ones.

Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image

In this paper, the Lung Cancer Classification using Convolutional Neural Network with DenseNet-201 Transfer Learning model optimized through Namib Beetle Optimization Algorithm on CT image (AtCNN-DenseNet-201 TL-NBOA-CT) is proposed. The input data are gathered from the CT Lung image. In pre-processing section, it removes the noise and enhances input images by Modified Sage-Husa Kalman Filtering (MSHKF).The pre-processed image is given into feature extraction phase. Then, six statistical features including mean, variance, entropy, energy, Average Amplitude, kurtosis are extracted based on Improved Empirical Wavelet Transforms (IEWT). Then, the extracted features are given to the Attention-based Convolutional Neural Network with DenseNet-201Transfer Learning (AtCNN-DenseNet-201 TL) to classify the Cancer and Non-Cancer of the CT image, where batch normalization layer of At CNN eliminated and added by DenseNet-201 layer. Namib Beetle Optimization Algorithm (NBOA) proposed in this work to enhance the AtCNN-DenseNet-201 classifier, which precisely classifies the lung cancer. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method is implemented and the effectiveness is assessed with some performance measures. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method attains 18.30 %, 21.37 % and 23.07 % greater precision, 24.84 %, 16.32 % and 31.36 % greater accuracy and 14.32 %, 21.97 % and 23.38 % greater F1-Score compared with existing techniques like detection and categorization of lung cancer CT pictures utilizing improved deep belief network along Gabor filters (CLC-CT-EDBN), presented categorization of lung cancer CT pictures utilizing a three dimensional deep CNN with multi-layer filter (CLC-CT-3D-DCNN), and novel hybrid deep learning technique for early identification of lung cancer utilizing neural networks (CLC-CT-3D-CNN) respectively.

Separation of molten electrolyte from the graphene nanocarbon product subsequent to electrolytic CO2 capture

The molten electrolysis of CO2 and its simultaneous transformation to graphene nanocarbons is a growing path to decarbonization of both anthropogenic CO2, and CO2 directly from the air. By tuning the electrolysis conditions a variety of pure graphene nanocarbons are produced from CO2. The carbon in CO2 is transformed at the cathode, growing as a carbanogel containing a matrix of the Graphene NanoCarbons (GNCs) and the molten electrolyte. This study demonstrates that one GNC product, carbon nanotubes from CO2, can be effectively separated from the carbanogel by removing the majority of the electrolyte for reuse in the electrolysis chamber. A molten electrolyte extraction efficiency of 98.5% from the carbanogel is achieved using filtration at high temperature and pressure. Optimization of the (1) press time, (2) filtration pressure applied to the carbanogel, and (3) filter type leads to a sequential increase in optimization. An increase of press time from 5 to 17 ​min increases the electrolyte extraction from 53.8% to 92% at 540 psi, and to 97.8% at 3700 psi. An increase in electrolyte extraction of 98.5% from the carbanogel occurs with the inclusion of a Dutch-weave screen in the multilayer filter. The optimization is conducted on 10 ​kg carbanogel samples, but instrumentation for up to 0.25-tonne carbanogel electrolyte separation is shown.

Investigating the improvement of the quality of industrial effluents for reuse with added processes: coagulation, flocculation, multi-layer filter and UV

Reuse of wastewater is one of the ways to develop water resources. In addition to the need for drinking water, many industries also need high-quality water in the production line. Therefore, the purpose of the present study is to investigate the advanced treatment of the wastewater treatment plant of Morche Khort industrial town using the processes of coagulation, flocculation with aeration, multi-layer filter, and disinfection by ultraviolet radiation to increase the quality of wastewater and reuse it in industries. In this study, to investigate the effect of coagulation and flocculation units along with aeration, filtration, and disinfection by ultraviolet rays (UV), on the quality of the secondary effluent from the wastewater treatment plant of Morche Khort industrial town, they were operated on a pilot scale. Polyaluminum chloride (PAC) was used as a coagulant. Layering of three layers of sand filter, from bottom to top including granulated silica at a height of 10 cm, sand at a height of 20 cm, and activated carbon at a height of 70 cm was used. The input and output sampling points of each unit were considered. By repeating twice in five stages of flow rates of 1, 2, 4, 6, and 8 (L/min), the samples were collected to determine COD, TSS, TDS, turbidity, pH, hardness, total coliform, and fecal coliform. Jar test results showed that Alum coagulant works almost the same as PAC in removing turbidity, but the efficiency of removing organic substances by PAC coagulant is higher than that of Alum at lower doses. The results of this study showed that the efficiency of the coagulation and flocculation process in removing turbidity, COD, TSS, TDS, and fat was 56.88%, 46.66%, 38%, 23.19%, and 91.43% respectively. In the current study, the results of the wastewater entering the sand filter showed that the percentage of removal efficiency with a loading rate of 1 (L/min) was turbidity, TSS, COD, TDS, and fat was 16. 93%, 56.84%, 50%, 5.67%, 33.44% respectively. In the UV disinfection unit, the removal efficiency percentage with a loading rate of 1 (L/min) for COD, TSS, turbidity, hardness, total coliform, and fecal coliform is 16%, 3.45%, 3.58%, 5.21%, 99.88%, and 98.37% respectively. Coagulation and flocculation system—multi-layer filter and disinfection can remove chemical–physical and microbial parameters to an acceptable level for using water in advanced purification systems and also for irrigation.

High-performance plasmonic mid-infrared bandpass filters by inverse design.

Plasmonic spectral filters composed of periodic nanostructured metal films offer novel opportunities for the development of multispectral imaging technologies in the mid-infrared region. However, traditional plasmonic filters, which typically feature simplistic structures such as nanoholes or nanorings, are constrained by a narrow bandpass and significant crosstalk, leading to limited practical performance. Filters designed using inverse techniques allow a substantial degree of freedom in creating intricate structures that align with desired spectral characteristics, including a quasi-square spectral profile, high transmission, wide full width at half maximum, and reduced crosstalk. In this study, we have utilized an inverse design algorithm to engineer high-performance bandpass filters for the mid-infrared range, achieving an average transmittance exceeding 80% within the bandpass window and below 10% in the stop band, which is comparable to that of commercial multilayer Bragg filters. Nanofabrication processes were employed to transfer the designed pattern into the gold film on ZnS substrate that is transparent in the mid-infrared range. The resulting filters exhibit spectral performance analogous to that of the inversely designed models, making them suitable for direct integration with mid-infrared photodetector arrays in multispectral imaging systems.