Filtering Scheme Research Articles

Evolutionary topology optimization with stress control for composite laminates using Tsai-Wu criterion

In this study, a topology optimization technique with stress control is proposed for the composite laminates. The bi-directional evolutionary structural optimization (BESO) method is selected to avoid the stress singularity. The technique expresses the failure index based on the Tsai-Wu criterion, thereby ensuring a comprehensive consideration of the anisotropy. To address the local nature of stress and multi-layer structure of laminates, a nested p-norm is employed, providing a global approximation of the local maximum failure index. To mitigate the highly non-linear stress behaviors, filter schemes are applied to both sensitivity numbers and design variables. Sensitivity numbers are derived via adjoint sensitivity analysis and Lagrange multipliers to address stress-based and stress-constrained problems. The proposed framework is validated through systematic numerical studies across various examples and conditions, offering insights into the topological behaviors of composite laminates. This study underscores the importance of considering distinct tensile and compressive strength in composite materials, which represents a key innovation. Additionally, the relationships among stiffness-based, stress-constrained, and stress-based designs are explored in depth.

Unsupervised fault detection using frequency-wise angular filtering in contaminated vibration signals

Manufacturing processes involve multiple machines within a production line. Unexpected faults in machines reduce productivity and increase maintenance costs. Engineers face difficulties in managing numerous machines individually and controlling them immediately. For automatic condition monitoring, several studies have focused on multivariate statistical process control and fault detection based on artificial intelligence. These methods require labeled data or assume that the training data contains only normal patterns. However, obtaining labeled data in the industry is challenging because engineers must manually label the data. Contaminated signals containing fault patterns in unlabeled training data significantly degrade the performance of fault detection in the model. This study proposes Unsupervised fault detection with Frequency-wise Angular Filtering (UFAF) to improve the performance of fault detection in contaminated vibration signals. The UFAF extracts angular features to estimate the normal samples for use only during model training. This filtering strategy is repeated at every epoch and is eventually optimised to use only high-quality normal samples during model training. An experiment using SpectraQuest gearbox datasets confirms the excellent performance for contaminated signals, as angular features are effective in identifying normal and fault signals. The UFAF is practical and applicable in industries wherein it is difficult to collect labeled data.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Expanding the landscape of systemic sclerosis-related autoantibodies through RNA immunoprecipitation coupled with massive parallel sequencing

ObjectivesSystemic sclerosis (SSc)-related autoantibodies are widely used diagnostic and prognostic biomarkers. This study aimed to develop a new assay for detecting anti-ribonucleoprotein autoantibodies in SSc based on RNA immunoprecipitation (RNA IP) coupled with massive parallel sequencing. MethodsSerum samples and clinical data were collected from 307 SSc patients. Among these, 57 samples underwent analysis using a new protocol that combines RNA IP with massive parallel sequencing (RIP-Seq). Filtering strategies and statistical outlier detection methods were applied to select RNA molecules that could represent novel ribonucleoprotein autoantigens associated with SSc. ResultsAmong the 30,966 different RNA molecules identified by RIP-Seq in 57 SSc patients, 197 were ultimately selected. These included all RNA molecules previously identified by RNA IP, which were found to exhibit high counts almost exclusively in samples positive for the autoantibodies associated to the corresponding RNA molecule, indicating high sensitivity and specificity of the RIP-Seq technique. C/D box snoRNAs were the most abundant RNA type identified. The immunoprecipitation patterns of the detected C/D box snoRNAs varied among patients and could be associated with different clinical phenotypes. In addition, other ribonucleoproteins were identified, which could be potential targets for previously undescribed SSc-related autoantibodies. These include H/ACA box snoRNPs, vault complexes, mitochondrial tRNA synthetases, and 7SK snRNP. ConclusionA novel RIP-Seq assay has been developed to detect autoantibodies targeting ribonucleoprotein complexes in SSc patients. This method successfully identified RNA molecules associated with ribonucleoproteins known to be targeted by SSc-related autoantibodies, validating both the assay and the analysis strategy. Additionally, this approach uncovered RNA molecules associated with ribonucleoproteins that were not previously identified as targets of SSc patients’ sera, suggesting potential new autoantibody candidates in this disease.

An inexact restoration direct multisearch filter approach to multiobjective constrained derivative-free optimization

Direct Multisearch (DMS) is a well-established class of methods for multiobjective derivative-free optimization, where constraints are addressed by an extreme barrier approach, only evaluating feasible points. In this work, we propose the replacement of this extreme barrier approach by a filter strategy, combined with an inexact feasibility restoration step, to address constraints in the DMS framework. The filter approach treats feasibility as an additional component of the objective function that needs to be minimized. The inexact restoration step attempts to generate new feasible points, contributing to prioritize this feasibility, a requirement for the good performance of any filter approach. Theoretical results are provided, analysing the different types of sequences of points generated by the new algorithm, and numerical experiments on a set of nonlinearly constrained biobjective problems are reported, stating the good algorithmic performance of the proposed approach.

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Assessing Statistical Methods for Generating Forecasts for COVID-19

The COVID-19 pandemic, a persistent global health emergency that has affected almost all facets of daily life, was initially discovered in Wuhan, China, in December 2019. Since that time, the virus has rapidly spread over the globe, causing serious social and economic upheavals necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile real-world data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing and filtering strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. This study also discovered that the ARIMA model, when applied directly to the original data without using any smoothing or filtering approaches, failed to forecast adequately, thereby demonstrating the insufficiency of the ARIMA model on its own to provide credible forecasts when given a volatile set of data.

Instantaneous detection and location of contaminant sources in intelligent buildings: A distributed Kalman filter scheme

Indoor Air Quality (IAQ), as a common concern of Intelligent Buildings (IBs), is easily compromised by contaminants which may result from accidents, pathogens or terrorist attacks. Once some contaminant occurs in one zone, it will be quickly diffused to other zones through windows, doors or Heating, Ventilation and Air Conditioning (HVAC) systems, which makes detecting and locating the contaminant source a significant challenge. In this paper, a distributed Kalman filter (DKF) based scheme for detecting and locating the multiple contaminant sources is developed. Firstly, a distributed state-space model of contaminant diffusion is established with the help of the multizone simulation program-CONTAM and the Multi-Chamber theory. Then, a multi-agent based distributed monitoring framework is constructed, where each agent equipped for each zone runs a Kalman filter using both the information of the local zone and the residuals of the interconnected zones. The decoupling strategy is employed to determine the filter gains to guarantee the optimal contaminant concentration estimations. Compared with existing methods, the proposed scheme can detect and locate multiple contaminant sources instantaneously. Meanwhile, it has the advantages of low computation and communication consumptions. A model of a realistic 11-zone experimental center created on CONTAM is used to demonstrate the effectiveness and superiority of the proposed scheme.

Improving weather forecast skill of the Korean Integrated Model by assimilating Soil Moisture Active Passive soil moisture anomalies

AbstractThis study investigates the potential benefit of assimilating soil moisture (SM) data retrieved from Soil Moisture Active Passive (SMAP) in improving global SM estimates and enhancing the weather forecast skill of the Korean Integrated Model (KIM). The 36‐km SMAP L2 SM retrievals are assimilated into the Noah land surface model (LSM) using the ensemble Kalman filter scheme through the National Aeronautics and Space Administration Land Information System (LIS) that is weakly coupled to KIM. A suite of cycling experiments of the KIM–LIS system that includes land and atmospheric data assimilation (DA) and five‐day weather forecasts are conducted over the global domain from March to July 2022. In the SMAP SM DA, two different SM bias correction methods, namely the cumulative distribution function matching and anomaly‐based bias correction, are applied to correct systematic biases between SMAP and Noah‐LSM before assimilation. The global triple collocation analysis reveals that compared to the control case (without SM DA), significant improvements in the global SM estimates are achieved by assimilating the SMAP data, especially by employing the anomaly‐based bias correction. Notably, the improved SM initial conditions lead to an improved screen‐level specific humidity and air temperature analysis and forecasts when the results are compared against the European Centre for Medium Range Weather Forecasts‐Integrated Forecasting System analysis. The beneficial impacts of the SMAP DA on the atmospheric variables extend up to an atmospheric level of 700 hPa. Prominent improvements in the KIM forecast skill by the SMAP DA applying the anomaly‐based bias correction are observed in the northern part of Africa and West and Central Asia with stronger impacts for longer forecast lead time. This paper demonstrates the feasibility of assimilating the SMAP data within KIM–LIS to enhance the KIM weather forecasts in the lower atmosphere when a proper SM bias correction method is applied.

한국 대학생 영어학습자의 외국어 불안감 및 학습전략에 대한 수준별 분석

This study analyzed the relationship between foreign language anxiety and language learning strategies among EFL learners at different English proficiency levels. The study involved 195 participants and examined the correlation between their levels of foreign language anxiety and their use of learning strategies through a Google survey, followed by in-depth interviews. The findings revealed that learners at high proficiency levels exhibited moderate language anxiety, and metacognitive strategy was found to be the most effective in managing anxiety at this level. At the intermediate level, cognitive strategy significantly helped reduce foreign language anxiety. Learners at low proficiency levels displayed higher anxiety, particularly regarding test and communication, but social and metacognitive strategies were effective in alleviating this anxiety. These results underscored the critical role of targeted learning strategies in mitigating foreign language anxiety, offering valuable insights for educators in developing instructional methods or educational content that apply affective filter strategies according to proficiency levels to enhance language acquisition.

An analysis of second-order sav-filtered time-stepping finite element method for unsteady natural convection problems

This paper presents an unconditionally stable time-filtering algorithm for natural convection equations. The algorithm is based on the scalar auxiliary variables in the exponential function and adopts a completely discrete Back-Euler combining time filter scheme. The proposed scheme requires minimal invasive modification of the existing program to improve the time accuracy from first-order to second-order without increasing the computational complexity, and we demonstrate the unconditional stability of the proposed algorithm and analyze its second-order convergence. In addition, due to the increasing demand for low-memory solvers, the application of a time-adaptive algorithm can improve the accuracy and efficiency of the proposed algorithm, so we extend the method to variable step sizes and construct an adaptive algorithm. Finally, the effectiveness of the proposed method and the accuracy of the theoretical results are verified by numerical experiments.

High-sensitivity miniaturized spectrometers using photonic crystal slab filters.

Miniaturized spectrometers have emerged as pivotal tools in numerous scientific and industrial applications, offering advantages such as portability, cost-effectiveness, and the capability for onsite analysis. Despite these significant benefits, miniaturized spectrometers face critical challenges, particularly in sensitivity. Reduced dimensions often lead to compromises in optical path length and component quality, which can diminish detection limits and limit their applications in areas such as low-light-level measurements. Here we developed a compact spectrometer that integrates an array of photonic crystal slab filters with band-stop spectral transmission characteristics into an image sensor. Compared to traditional gratings or bandpass filter strategies, where each detector can only read light of a single wavelength component, our band-stop strategy allows each detector to read the light of all wavelengths except the band-stop wavelength. This maximizes energy extraction from incident signals, significantly improving the sensitivity of the spectrometer. Spectral reconstruction is achieved mathematically using pre-calibrated band-stop responses combined with a single coded image. Our spectrometer delivers a spectral resolution of 1.9 nm and demonstrates sensitivity more than ten times greater than that of conventional grating spectrometers during fluorescence spectroscopy of Ascaris lumbricoides. The design is fully compatible with complementary metal-oxide-semiconductor (CMOS) technology, allowing for mass production at low costs and thus promising broad deployment in sensitive applications.

Practical feature filter strategy to machine learning for small datasets in chemistry

Many potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different dimensions for further productive predictions as being most relevant by using our feature filter strategy. The best extreme gradient boosting regression model possesses a good performance and is evaluated from statistical and theoretical perspectives, reaching a level of accuracy comparable to density functional theory computations and allowing for physical interpretations of the predictions. Overall, the results indicate that the feature filter strategy can help interdisciplinary scientists without rich professional AI knowledge and limited computational resources to establish a reliable small training dataset first, which may make the final machine learning model training easier and more accurate, avoiding time-consuming hyperparameter explorations and improper feature selection.

A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions

As essential components of machinery equipment, rolling bearings directly affect the safety of the machinery equipment. The timely diagnosis of bearing faults can effectively prevent equipment lapses. However, bearings are often inconsistently distributed. This has resulted in a significant decrease in their availability. Moreover, the performances of traditional models are poor when fault samples are scarce. The unsupervised domain adaptation (UDA) model based on the transfer learning theory can solve the above problems in static scenarios. However, source domain data are often not directly accessible for privacy protection. Therefore, achieving the robustness of UDA models is significantly challenging. Source-free UDA can achieve a positive transfer from the source domain to the target domain based only on a pretrained source-domain model and unlabeled target-domain data. In this study, we built a source-free robust UDA approach with pseudo-label uncertainty estimation (SFRDA-PLUE) for diagnosing bearing faults using a limited number of samples. First, we designed a robust feature extractor (SANet) and proposed a novel binary soft-constrained information entropy. This was applied to solve the problem that standard information entropy cannot effectively estimate the uncertainty of pseudo-labels. In addition, we constructed a weighted comparison filter strategy to smoothen the fuzzy samples. Finally, we introduced an information-maximizing loss strategy to optimize the performance of the source domain classifier and the pseudo-label estimator. Thus, the robustness of the pseudo-label uncertainty estimation was significantly improved. The experimental results validated that the SFRDA-PLUE approach can achieve excellent diagnostic performance under a limited number of samples.

Enhancing TDCP-specified GNSS/INS tightly coupled filter robustness in cascaded for low-cost urban applications

Abstract Integration of global navigation satellite systems (GNSS) with inertial navigation systems (INS) is commonly employed to enhance navigation performance. However, conventional integrated algorithms suffer from low-quality GNSS measurements due to either inaccurate pseudorange or difficulty in ambiguity resolution when utilizing carrier phase (CP) measurements in urban environments. We propose a new time-differenced CP (TDCP-) specified GNSS/INS tightly coupled integration algorithm for low-cost urban applications. The proposed method employs a dual-rate filter scheme, wherein the potential of TDCP observations and INS-derived positions are fully harnessed for state estimation with PR outlier inhibition and CP small cycle slip repair. Field test results in urban environments demonstrate that the integrated navigation scheme proposed is superior to conventional algorithms, with an average improvement of about 13.4%, 19.2%, and 25.5% in position, velocity, and attitude, respectively.

A combined denoising method for Q-factor compensation of poststack seismic data

Attenuation is a main factor limiting the resolution of seismic data. Earth works as a low-pass filter, which has strong attenuation of the high-frequency data. The loss of high-frequency energy can be compensated by the inverse Q filtering strategy. However, this method will also increase the energy of random noise which limits its application. The inverse Q filtering algorithm also needs the Q-factor as the input parameter, which is not easy to obtain. In this paper, we proposed a three-stage process to correct the attenuation of poststack data. In the first stage, a robust structure-oriented filtering is applied to remove random noise while protecting the structure information to avoid high-frequency noise burst. In the second stage, the local centroid frequency shift (LCFS) method is used to estimate the Q factor along the seismic trace. This method combined shaping regularization and centroid frequency shift (CFS) method to improve the robustness and accuracy of Q estimation to some extent. The final stage is to apply a stable inverse Q-filtering. Synthetic and field data examples demonstrate that time-varying Q-value can be accurately estimated by using the local centroid frequency shift (LCFS) method, and the proposed workflow can compensate the attenuation without bursting of high-frequency random noise.

Optimization Study of High-Efficiency Rectifier Circuits for Uninterruptible Power Supply Systems

Uninterruptible Power Supply (UPS) is a kind of equipment that can supply power to other equipment’s in an uninterrupted way, which can ensure that the key equipment’s can continue to operate stably in the face of power grid failure. Therefore, uninterruptible power supply system is of utmost importance in the modern power system. The aim of this paper is to optimize rectifier circuits in uninterruptible power supply system that rely on three-phase bridge full-wave rectifier circuits. Build a model using LTspice and simulate it, compare and analyze the waveform characteristics of the output voltage of the load under different filtering schemes, and propose optimization strategies based on actual production. As one of the key components of the UPS system, the rectifier circuit can directly affect the efficiency with stability of the whole system. Hence, it is important to optimize the high efficiency rectifier circuit to improve the overall performance of the UPS system.

V2G Grid-Tied Inverter System based on LCL Filter with Control Strategy

Vehicle-to-Grid (V2G) technology has been hotly discussed in recent years because of its broad prospects in load transfer and regulation. This article proposes a grid-connecting inverter system with LCL filter and control strategy for use in electric vehicle (EV) applications. A review of designation of LCL filter’s parameter and mathematic modelling is given in part 2. An explanation of control strategy is given in part 3. Part 4 gives a comparison of active damping and passive damping control strategies with calculated parameters and gives the simulation results based on MATLAB. The results show with parameters calculated with method in article, both strategies have good tracking performance and similar response time. For the critical steady state, passive damping shows higher amplitude of oscillation. This phenomenon gives a new view proof that active damping method is superior than passive damping control strategy. The article is also of some reference value for determining LCL filter’s parameters and proportional, integral coefficient of similar control strategy.

Robust Design of Electrical System for Nuclear Plants to Prevent a Nuclear Accident During an Emergency Situation

The Chernobyl accident taught us an important lesson regarding the significance of the emergency diesel generator (EDG), which is crucial for supplying a nuclear facility with emergency alternating-current power. A worst-case scenario would occur if the EDG failed to start functioning. In response to this problem, this research addresses the issue and suggests an optimal design for another alternative electrical source using solar energy. Until now, employing this technology presented a challenge as a solar energy system generates electrical harmonics that are transmitted from the photovoltaic (PV) grid to the electrical network. These harmonics can negatively impact the performance and operation of nuclear facilities. Consequently, this study proposes a comprehensive investigation that tackles this issue by implementing a LCL filter to mitigate the harmonics in the PV network. This paper not only provides an alternative solution in the event of a diesel generator breakdown, but also studies the effect of this solution on the station’s performance and how to improve it. A mathematical model based on the MATLAB program is developed to simulate the application of the LCL filter strategy. The proposed technique is tested through simulation with an actual case, demonstrating its reliability and high-quality outcomes.

Polarity-controllable magnetic skyrmion filter

Abstract The skyrmion generator is one of the indispensable components for the future functional skyrmion devices, but the process of generating skyrmion cannot avoid mixing with other magnetic textures, such as skyrmionium and nested skyrmion bags. These mixed magnetic textures will inevitably lead to the blockage of skyrmion transport and even the distortion of data information. Therefore, the design of an efficient skyrmion filter is of great significance for the development of skyrmion-based spintronic devices. In this work, a skyrmion filter scheme is proposed, and the high-efficiency filtering function is demonstrated by micromagnetic simulations. The results show that the filtering effect of the scheme depends on the structure geometry and the spin current density that drives the skyrmion. Based on this scheme, the polarity of the filtered skyrmion can be controlled by switching the magnetization state at the output end, and the “cloning” of the skyrmion can be realized by geometric optimization of the structure. We believe that in the near future, the skyrmion filter will become one of the important components of skyrmion-based spintronic devices in the future.