Filter Technology Research Articles

Sunscreens - another endangered species?

Skin cancer continues to increase in incidence year-on-year and represents the most common form of cancer across the globe. Every human undergoes premature ageing, particularly on the face, neck and hands. Both phenomena are driven primarily by chronic, daily exposure to solar ultraviolet radiation (UVR). While sunscreen products play a primary role in the prevention of UVR skin damage, the active ingredients, i.e., UVR filters, are facing unprecedented challenges in the coming 10 years and their future is by no means certain. This article, therefore, reviews afresh the facts around photoprotection and the role of sunscreen products in the prevention of acute (sunburn) and chronic (cancer, photoageing) skin damage and compares/contrasts these with various emerging questions and opinions around UVR filter technology. We present a passionate defence of this remarkable technology, but also attempt to imagine a world without it.

The Urgency of the Role and Concept of Digital Literacy in Education in the Disruption Era (21st Century)

In addition to entering the era of globalization with the start of the AEC (Asian Economic Community) and the incessant life of the 21st century, the world is currently experiencing super- fast changes marked by a new era or referred to as the era of the industrial revolution 4.0. This high competence is of course a special concern for education as the main guard for competency development. One of the most important components in this field is of course educators. An educator in this era is required to be able to make digital natives (children who know technology from birth) as human beings who will not lose and make mistakes in taking steps and in utilizing existing technology, bearing in mind that in addition to great benefits, technology is quite a threat to the development of students. Educators must be able to select and filter technology and insert value into their learning with digital literacy. So, of course this is of particular concern so that educators have this digital literacy competence. Strengthening literacy, numeracy and technological adaptation are important efforts to improve the quality of human resources in the Digital Age and the Disruption Era. Indonesia is very far behind other countries in terms of this capability. The government as well as related parties (Schools, Universities, Education Quality Assurance Institutions, and Parents) must participate in strengthening literacy, numeracy, and technology adaptation for students and educators in Indonesia. One of the activities is integrating literacy, numeracy and technology activities in learning either at school or at home.

Efficient trapping and destruction of SARS-CoV-2 using PECO-assisted Molekule air purifiers in the laboratory and real-world settings

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted human-to-human via aerosols and air-borne droplets. Therefore, capturing and destroying viruses from indoor premises are essential to reduce the probability of human exposure and virus transmission. While the heating, ventilation, and air conditioning (HVAC) systems help in reducing the indoor viral load, a targeted approach is required to effectively remove SARS-CoV-2 from indoor air to address human exposure concerns. The present study demonstrates efficient trapping and destruction of SARS-CoV-2 via nano-enabled filter technology using the UV-A-stimulated photoelectrochemical oxidation (PECO) process. Aerosols containing SARS-CoV-2 were generated by nebulization inside an air-controlled test chamber where an air purifier (Air Mini+) was placed. The study demonstrated the efficient removal of SARS-CoV-2 (99.98 %) from the test chamber in less than two minutes and PECO-assisted destruction (over 99%) on the filtration media in 1 h. Furthermore, in a real-world scenario, the Molekule Air-Pro air purifier removed SARS-CoV-2 (a negative RT-qPCR result post-running the filter device) from the circulating air in a COVID-19 testing facility. Overall, the ability of two FDA-approved class II medical devices, Molekule Air-Mini+ and Air-Pro air purifiers, to remove and destroy SARS-CoV-2 in indoor settings was successfully demonstrated. The study indicates that as the "tripledemic" of COVID-19, influenza, and respiratory syncytial virus (RSV) overwhelm the healthcare facilities in the USA, the use of a portable air filtration device will help contain the spread of the viruses in close door facilities, such as in schools and daycare facilities.

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.

Experimental Investigation of the Frequency Response of an LC-Filter and Power Transformer for Grid Connection

The power delivered by a voltage source inverter needs to be filtered to fulfill grid code requirements. A commonly used filter technology is the LCL-filter. An issue with the LCL-filter is the occurrence of a resonance peak, which can be mitigated with active or passive damping methods. The transfer function of the filter is often used to investigate the frequency response of the system and propose damping methods. The use of an LC-filter combined with a power transformer to form an LCL-filter has not been extensively investigated. Therefore, the study in this article introduces a model for an LC-filter and power transformer for the grid connection and a derived transfer function for the model. The transfer function for the system is validated with simulations and experimental investigations. The results from simulations and the results from a direct solution of the derived analytical function overlap almost perfectly. The magnitudes of the experimental results are approximately 1 dB lower than the simulation and analytical results before the resonance frequency. At the resonance frequency, the experimental results are approximately 13.4 dB lower. The resonance frequency, however, occurs at approximately the same frequency. It is also concluded that the system is significantly damped.

Adaptive Machine Learning-Based Proactive Thermal Management for NoC Systems

Because of the high-complex interconnection in contemporary multicore systems, the network-on-chip (NoC) technology has been proven as an efficient way to solve the communication problem in multicore systems. However, the thermal problem becomes the main design challenge in the current NoC systems due to the high-diverse workload distribution and large power density. Therefore, proactive dynamic thermal management (PDTM) is employed as an efficient way to control the system temperature. Based on the predicted temperature information, the PDTM can control the system temperature in advance to reduce the performance impact during the temperature control period. However, conventional temperature prediction models are usually built based on specific physical parameters, which are usually temperature-sensitive. Consequently, the current temperature prediction models still result in significant temperature prediction errors. To solve this problem, a novel adaptive machine learning (ML)-based PDTM is proposed in this work. The adaptive ML-based PDTM first uses an adaptive single layer perceptron (ASLP), which is composed of a single-neuron operation and a least mean square (LMS) adaptive filter technology, to precisely predict the future temperature. Afterward, the proposed adaptive reinforcement learning (RL) is used to find the proper throttling ratio to control the system temperature. In this way, the proposed adaptive ML-based PDTM can adapt to the hyperplane of the temperature behavior of the NoC system and provide a proper temperature control strategy at runtime. Compared with related works, the proposed approach reduces average temperature prediction error by 0.2%–78.0% and improves the system performance by 2.4%–43.0% with smaller hardware overhead.

Retracted: Recognition and Error Correction of Piano Playing Features Based on Filtering Technology

Retracted: Recognition and Error Correction of Piano Playing Features Based on Filtering Technology

A review of regeneration mechanism and methods for reducing soot emissions from diesel particulate filter in diesel engine.

With the global emphasis on environmental protection and the proposal of the climate goal of "carbon neutrality," countries around the world are calling for reductions in carbon dioxide, nitrogen oxide, and particulate matter pollution. These pollutants have severe impacts on human lives and should be effectively controlled. Engine exhaust is the most serious pollution source, and diesel engine is an important contributor to particulate matter. Diesel particulate filter (DPF) technology has proven to be an effective technology for soot control at the present and in the future. Firstly, the exacerbating effect of particulate matter on human infectious disease viruses is discussed. Then, the latest developments in the influence of key factors on DPF performance are reviewed at different observation scales (wall, channel, and entire filter). In addition, current soot catalytic oxidant schemes are presented in the review, and the significance of catalyst activity and soot oxidation kinetic models are highlighted. Finally, the areas that need further research are determined, which has important guiding significance for future research. Current catalytic technologies are focused on stable materials with high mobility of oxidizing substances and low cost. The challenge of DPF optimization design is to accurately calculate the balance between soot and ash load, DPF regeneration control strategy, and exhaust heat management strategy.

Robust adaptive dynamic memory‐event‐triggered attitude control for nonlinear multi‐UAVs resist actuator hysteresis

SummaryThis article studies the memory‐based dynamic event‐triggered (MBDET) robust adaptive fuzzy control problem for a class of uncertain nonlinear MASs subject to Backlash‐Like Hysteresis. The considered MASs are modeled as a class of nonlinear leader‐following multiagent systems with multiple inputs for each subsystem, which can be used to model multiple multi‐rotor unmanned aerial vehicle (Multi‐UAV) attitude systems. In the framework of backstepping recursive design, the fuzzy approximation technology, the parameter estimation technology, and the first‐order filtering technology are integrated, and an effective fuzzy adaptive control method is proposed, which solves the problems of Backlash‐Like Hysteresis and external disturbances in the system while avoiding the “explosion of complexity”. In order to relieve the communication pressure of Multi‐UAV attitude systems, a novel dynamic memory‐event‐triggered mechanism (DMETM) based on user‐set parameters is developed. Compared with the traditional event‐triggered mechanism, this DMETM has larger triggering time intervals, effectively reduces communication frequency, and has greater flexibility in balancing system performance and communication bandwidth resource constraints. The closed‐loop stability analysis scheme for Multi‐UAV attitude systems is presented by combining the backstepping recursive design technique and Lyapunov stability theory, which proves that closed‐loop stability and tracking performance can be ensured. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the dynamic memory‐event‐triggered attitude control problem for nonlinear Multi‐UAVs.

Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and color filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR, 8–14 μm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum (Al)-based plasmonic filter array sandwiched in germanium (Ge). To realize the multispectral sensor, the filter arrays are then integrated into a three-dimensional (3D) printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.

Robust Filter Method for SINS/DVL/USBL Tight Integrated Navigation System

In order to solve the problem of SINS/DVL/USBL system interference in complex underwater environment. An asynchronous sequential robust filter method is proposed in this paper. The original output azimuth, slant range and altitude of the ultra-short baseline as the measurement information, a compact combination model is established. The problem of large amount of computation caused by the high measurement dimension of multi-sensor information fusion is solved by introducing asynchronous sequential filter technology. Meanwhile, a robust Kalman filter algorithm is proposed, which based on Mahalanobis distance. The outlier detection is designed based on statistical characteristics. Finally, the effectiveness of the method proposed in this paper is verified by simulation and River experiments. The experimental results show that the method proposed in this paper can effectively eliminate outliers and improve the navigation accuracy while reducing the amount of computation.

Modal-Transition-Induced Valleys of K2 in Piezoelectric Bilayer Laterally Vibrating Resonators.

Piezoelectric Laterally Vibrating Resonators (LVRs) have attracted significant attention as a potential technology for next-generation wafer-level multi-band filters. Piezoelectric bilayer structures such as Thin-film Piezoelectric-on-Silicon (TPoS) LVRs which aim to increase the quality factor (Q) or aluminum nitride and silicon dioxide (AlN/SiO2) composite membrane for thermal compensation have been proposed. However, limited studies have investigated the detailed behaviors of the electromechanical coupling factor (K2) of these piezoelectric bilayer LVRs. Herein, AlN/Si bilayer LVRs are selected as an example, we observed notable degenerative valleys in K2 at specific normalized thicknesses using two-dimensional finite element analysis (FEA), which has not been reported in the previous studies of bilayer LVRs. Moreover, the bilayer LVRs should be designed away from the valleys to minimize the reduction in K2. Modal-transition-induced mismatch between electric and strain fields of AlN/Si bilayer LVRs are investigated to interpret the valleys from energy considerations. Furthermore, the impact of various factors, including electrode configurations, AlN/Si thickness ratios, the Number of Interdigitated Electrode (IDT) Fingers (NFs), and IDT Duty Factors (DFs), on the observed valleys and K2 are analyzed. These results can provide guidance for the designs of piezoelectric LVRs with bilayer structure, especially for LVRs with a moderate K2 and low thickness ratio.

Finite-time command filter-based adaptive fuzzy output-feedback control for MIMO nonlinear systems with multiple actuator constraints

In this article, the finite-time adaptive fuzzy output-feedback control strategy is presented for a class of multi-input and multi-output (MIMO) nonlinear systems with multiple actuator constraints and unmeasured states. Fuzzy logic systems and a fuzzy adaptive state observer are adopted to estimate the nonlinear uncertainties and the unmeasured states, respectively. The inherent ‘explosion of complexity’ problem is eliminated by adopting command filter technology and the corresponding error compensation mechanism is exploited to alleviate the influence of the errors generated by command filters. It is further proved that all the signals can be fast finite-time bounded in the closed-loop systems based on the Lyapunov stability theory. Besides, the observer and tracking errors can reach a small region around the origin in fast finite time. Finally, the practicability of the presented theoretic result can be demonstrated through the numerical and practical examples.

A learning trajectory planning for vibration suppression of industrial robot

PurposeThis paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.Design/methodology/approachBased on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.FindingsBy building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.Originality/valueThe idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.

A new demand response management strategy considering renewable energy prediction and filtering technology

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work.

An Innovative model of magnetically intercalated expanded graphite for calculating radar attenuation performance at 2–18 GHz

With the emergence of various filtering technologies, the radar jamming efficiency of the technology based on radar cross section is ever lower, therefore cannot meet military requirements. In this context, the jamming technology based on attenuation mechanism has been developed and plays an increasingly important role in disturbing radar detecting. Magnetically expanded graphite (MEG) has excellent attenuation efficiency because it can cause dielectric loss as well as magnetic loss. Moreover, MEG features good impedance matching, which makes more incidence of electromagnetic waves into the material; and its multi-layer structure is conducive for electromagnetic wave reflection and absorption. In this work, the structure model of MEG was established by analyzing the layered structure of expanded graphite (EG) and the dispersion of intercalated magnetic particles. The electromagnetic parameters of thus-modeled MEG were calculated based on the equivalent medium theory; and effects of EG size, magnetic particle type and volume fraction on the attenuation performance were studied by the variational method. It is indicated that MEG with 500-μm diameter has the best attenuation effect and the highest increment of absorption cross section appears at 50% volume fraction of the magnetic particles at 2 GHz. The imaginary part of complex permeability of the magnetic material has the most significant influence on the attenuation effect of MEG. This study provides guidance for the design and application of MEG materials in disturbing radar detecting field.

Design Comparison between the Gravity and Pressure Sand Filters for Water Treatment, Review

Hygienic engineering has dedicated a lot of time and energy to studying water filtration because of how important it is to human health. Thorough familiarity with the filtration process is essential for the design engineer to keep up with and profit from advances in filtering technology and equipment as the properties of raw water continue to change. Because it removes sediment, chemicals, odors, and microbes, filtration is an integral part of the water purification process. The most popular technique for treating surface water for municipal water supply is considered fast sand filtration, which can be achieved using either gravity or pressure sand filters. Predicting the performance of units in water treatment plants is a basic principle. For that reason, this research was executed to compare gravity and pressure sand filters in terms of construction, use, efficiency, filtration rate, cost, benefit, and drawbacks to predict the performance of those units under different conditions and from an economic standpoint. It also served as a presentation and review of previous studies dealing with the evaluation and development of pressure and gravity filters. This paper gives a brief overview of filtration theory, the types and properties of filter media, filter backwashing, and operational problems that can be avoided in the filtration process.

Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.

Command filter-based adaptive fuzzy switching event-triggered control for non-affine nonlinear systems with actuator faults

This paper considers a problem of command filter-based adaptive fuzzy switching event-triggered control for non-affine nonlinear systems with actuator faults. First, a state observer is given to estimate unmeasurable state. Secondly, an error transformation equation is designed to constrain output tracking error, and the feasibility condition of tracking error is not required. Then, a finite time fault-tolerant control scheme is constructed based on command filter and backstepping technologies, which can compensate completely the effects of actuator faults and trigger errors, and there is no differential explosion, singularity phenomenon and Zeno behavior. Besides, a switching event-triggered algorithm is designed to reduce the triggering number under the premise of ensuring the tracking performance. Finally, the proposed algorithm is applied to an one-link manipulator to verify the availability.

Research on active power filter technology

A large number of power electronic devices used and transformed for the electrical energy in the grid has improved the characteristics and economic efficiency of the utility grid of the power system, but the nonlinear load components make the harmonic pollution in the grid more and more serious, which reduces the power quality of the power supply network. An active filter model based on ip-iq algorithm and PI control strategy is proposed and simulated to show that the filter can compensate for reactive power factor and harmonics and effectively suppress harmonics.