Filtering Scheme Research Articles

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.

Impact of missense mutations on the structure–function relationship of human succinyl-CoA synthetase using in silico analysis

The encephalomyopathic mtDNA depletion syndrome with methylmalonic aciduria is associated with succinyl-CoA synthetase (SCS) deficiency caused by pathogenic variants in genes encoding its two subunits. SCS is a mitochondrial enzyme involved in several metabolic pathways and acts as a heterodimer composed of α and β subunits encoded by SUCLG1 and SUCLA2 genes, respectively. The purpose of this study was to analyze the effects of the most pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) by applying, using different prediction tools, a filtering strategy, on the 343 and 365 nsSNPs found in SUCLG1 and SUCLA2 genes, respectively, retrieved from the databases, then to evaluate their structural and functional effects using homology modeling and molecular docking. Results showed that most deleterious mutations selected for structural analysis were located in loop regions critical for protein stability and function, especially, variants altering glycine and proline residues in these regions supporting their importance. We also showed that variants leading to hydrophobic and hydrophilic residues can destabilize the folding and binding of the protein. Molecular docking has also been used to identify the most important regions of ligand binding site (CoA binding site, ADP-Mg2+ binding site and phosphate ion binding site) and between the two subunits themselves, which mainly involving the ligase CoA domain. Our structural analysis, performed on selected nsSNP, are in accordance with experimental studies reported in the literature and predicted that they would responsible to either nonfunctional protein, subunit instability resulting in reduced amounts of misassembled protein, or in a protein unable to phosphorylate ADP.

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.

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.

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.

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.

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.

A comparison of impulsive noise metrics

Impulsive sounds are transient acoustic events that can draw undesirable attention because of their abrupt nature, for example sounds produced by weights dropping onto floors in buildings. A metric that represents human experience of these sounds can guide efforts to mitigate them. This paper investigates metrics developed for impulsive sounds generated by weight or ball drops and by IT equipment. The metric for IT equipment is an impulsiveness metric formulated with psychoacoustic bands and loudness descriptions. The metric for weight or ball drops is the fast time-weighted sound pressure level implemented with various filtering schemes. The ability of the metrics in predicting subjective human experience of IT equipment transients is compared. Metric values for sounds generated by ball drops onto various floor assemblies on a concrete slab are compared.

Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computing

AbstractFood computing has emerged as a promising research field, employing artificial intelligence, deep learning, and data science methodologies to enhance various stages of food production pipelines. To this end, the food computing community has compiled a variety of data sets and developed various deep-learning architectures to perform automatic classification. However, automated food classification presents a significant challenge, particularly when it comes to local and regional cuisines, which are often underrepresented in available public-domain data sets. Nevertheless, obtaining high-quality, well-labeled, and well-balanced real-world labeled images is challenging since manual data curation requires significant human effort and is time-consuming. In contrast, the web has a potentially unlimited source of food data but tapping into this resource has a good chance of corrupted and wrongly labeled images. In addition, the uneven distribution among food categories may lead to data imbalance problems. All these issues make it challenging to create clean data sets for food from web data. To address this issue, we present AutoCleanDeepFood, a novel end-to-end food computing framework for regional gastronomy that contains the following components: (i) a fully automated pre-processing pipeline for custom data sets creation related to specific regional gastronomy, (ii) a transfer learning-based training paradigm to filter out noisy labels through loss ranking, incorporating a Russian Roulette probabilistic approach to mitigate data imbalance problems, and (iii) a method for deploying the resulting model on smartphones for real-time inferences. We assess the performance of our framework on a real-world noisy public domain data set, ETH Food-101, and two novel web-collected datasets, MENA-150 and Pizza-Styles. We demonstrate the filtering capabilities of our proposed method through embedding visualization of the feature space using the t-SNE dimension reduction scheme. Our filtering scheme is efficient and effectively improves accuracy in all cases, boosting performance by 0.96, 0.71, and 1.29% on MENA-150, ETH Food-101, and Pizza-Styles, respectively.

Comparing triple and single Doppler lidar wind measurements with sonic anemometer data based on a new filter strategy for virtual tower measurements

Abstract. In this study, we compare the wind measurements of a virtual tower triple Doppler lidar setup to those of a sonic anemometer located at a height of 90 m above ground on an instrumented tower and with those of two single Doppler lidars to evaluate the effect of the horizontal homogeneity assumption used for single Doppler lidar applications on the measurement accuracy. The triple lidar setup was operated in a 90 m stare and a step–stare mode at six heights between 90 and 500 m above ground, while the single lidars were operated in a continuous scan velocity–azimuth display (VAD) mode where one of them had a zenith angle of 54.7° and the other one of 28.0°. The instruments were set up at the boundary-layer field site of the German Meteorological Service (DWD) in July and August of 2020 during the FESST@MOL (Field Experiment on sub-mesoscale spatiotemporal variability at the Meteorological Observatory Lindenberg) 2020 campaign. Overall, we found good agreement of the lidar methods for the whole study period for different averaging times and scan modes compared to the sonic anemometer. For the step–stare mode wind speed measurements, the comparability between the triple lidar and the sonic anemometer was 0.47 m s−1 at an averaging time of 30 min with a bias value of −0.34 m s−1. For wind speed measured by one single lidar setup for the same period with an averaging time of 30 min, we found a comparability of 0.32 m s−1 at an averaging time of 30 min and a bias value of −0.07 m s−1 as well as values of 0.47 and −0.34 m s−1 for the other one, respectively. We also compared the wind velocity measurements of the single and triple lidars at different heights and found decreasing agreement between them with increasing measurement height up to 495 m above ground for the single lidar systems. We found that the single Doppler lidar with the increased zenith angle produced poorer agreement with the triple Doppler lidar setup than the one with the lower zenith angle, especially at higher altitudes. At a height of 495 m above ground and with an averaging time of 30 min the comparability and bias for the larger zenith angle were 0.71 and −0.50 m s−1, respectively, compared to values of 0.57 and −0.28 m s−1 for the smaller zenith angle. Our results confirm that a single Doppler lidar provides reliable wind speed and direction data over heterogeneous but basically flat terrain in different scan configurations. For the virtual tower scanning strategies, we developed a new filtering approach based on a median absolute deviation (MAD) filter combined with a relatively relaxed filtering criterion for the signal-to-noise ratio output by the instrument.

Exploring the genetic and epigenetic underpinnings of early-onset cancers: Variant prioritization for long read whole genome sequencing from family cancer pedigrees.

Despite significant advances in our understanding of genetic cancer susceptibility, known inherited cancer predisposition syndromes explain at most 20% of early-onset cancers. As early-onset cancer prevalence continues to increase, the need to assess previously inaccessible areas of the human genome, harnessing a trio or quad family-based architecture for variant filtration, may reveal further insights into cancer susceptibility. To assess a broader spectrum of variation than can be ascertained by multi-gene panel sequencing, or even whole genome sequencing with short reads, we employed long read whole genome sequencing using an Oxford Nanopore Technology (ONT) PromethION of 3 families containing an early-onset cancer proband using a trio or quad family architecture. Analysis included 2 early-onset colorectal cancer family trios and one quad consisting of two siblings with testicular cancer, all with unaffected parents. Structural variants (SVs), epigenetic profiles and single nucleotide variants (SNVs) were determined for each individual, and a filtering strategy was employed to refine and prioritize candidate variants based on the family architecture. The family architecture enabled us to focus on inapposite variants while filtering variants shared with the unaffected parents, significantly decreasing background variation that can hamper identification of potentially disease causing differences. Candidate d e novo and compound heterozygous variants were identified in this way. Gene expression, in matched neoplastic and pre-neoplastic lesions, was assessed for one trio. Our study demonstrates the feasibility of a streamlined analysis of genomic variants from long read ONT whole genome sequencing and a way to prioritize key variants for further evaluation of pathogenicity, while revealing what may be missing from panel based analyses.

Adaptive-neural command filtered synchronization control of tele-robotic systems using disturbance observer with safety enhancement

Due to the nature of the tele-robotic systems, some main problems are imposed, such as external disturbances, time-varying delays and external torques. However, some applications such as tele-surgery systems, require high precision, safety and accurate transmission of information between the leader and follower robots. In order to facilitate the imposed restrictions and to guarantee transient-state, and even steady-state performances in the presence of external disturbances and system uncertainties, the time-varying full state constrained control is employed by applying the Barrier Lyapunov function (BLF). In this regard, a new adaptive neural network torque observer is proposed to make the system independent from the force sensors. Moreover, the independence of the proposed algorithm from perfect knowledge of the manipulator dynamics and time-delay’s derivative of communication channels are advantages of this paper. Furthermore, the key idea of this paper is to lessen the computational complexity in the backstepping-based adaptive neural controller by means of the command filter strategy and the BLF approach is combined to converge the synchronization error signals into a predefined constraint. In order to consider practical limitations, time-varying delays in the communication channel and input saturation constraint are also considered in the design mechanism. Finally, the stability analysis of the observer and controller together is conducted and the evaluation of the performance of the proposed method is performed through a series of various scenarios and comparisons.