Rejection Method Research Articles

Active Disturbance Rejection Control Based on Deep Reinforcement Learning of PMSM for More Electric Aircraft

In this article, an active disturbance rejection controller (ADRC) based on deep reinforcement learning (DRL) algorithm is proposed to be used in the flux weakening control (FWC) system of motors for more electric aircraft. Artificial intelligence algorithm is introduced into ADRC motor control system for the first time, and DRL is designed as the automatic tuning for the parameters optimization of ADRC. The interface module scheme is proposed to realize the conversion between the relevant quantities of the control system and the DRL Agent according to the characteristics of ADRC. The parameters are optimized in the form of parameter modification, and a new DRL-ADRC control framework is proposed which can avoid being trapped into local optimum. The ADRC model designed for the speed loop of FWC system are first introduced. An interface module is subsequently built to enable DRL to interact with the FWC system automatically. DRL agent is trained to optimize the internal parameters of ADRC, which have the characteristics of large quantities, weak sensitivity and strong coupling. Deep deterministic policy gradient is used as the strategy of DRL, which can quickly determine the descent gradient and converge the multiobjective optimization problem. Simulation and comparison with classical heuristic algorithms and disturbance rejection methods are carried out to show the superiority of DRL. The feasibility and effectiveness of the proposed control method are verified by experiments on an aerospace motor for MEA.

Chlorophyll fluorescence of wheat leaves when infected with Bipolaris sorokiniana

For three spring wheat cultivars, characteristics of adaptive reaction to cereal root rot pathogen Bipolaris sorokiniana Shoem. infection are found by laboratory experiments using chlorophyll fluorecence parameter (ChlF) dynamics of 10-16 day old seedlings. Such ChlF parameters as Y(II), ETR, qP, Fv / Fm, Fv / Fо, Y(NPQ), qN and Y(NQ) are verified as informative, being reliable biomarkers for photosynthetic apparatus level pre-symptom estimate of cultivar resistance to the pathogen. For more resistant cultivars Novosibirskaya 29 and Sibirskaya 21, the reaction to the pathogen invasion appeared to be less expressed compared to less resistant cultivar Novosibiskaya 41. More resistant cultivars have the smallest ChlF parameter changes compared to the control ones. Sixteen day old seedlings infected with B. sorokiniana showed the largest intercultivar differences. The photosynthetic activity level during adaptive reaction to B. sorokiniana pathogenesis can be used as stress resistance criterion for selection material to accelerate its sampling and to increase its effectiveness by early non-perspective sample rejection.

Accreditation of a molecular HIV diagnostic laboratory following the Strengthening Laboratory Management Towards Accreditation (SLMTA)-Stepwise Laboratory Quality Improvement Process Towards Accreditation (SLIPTA) approach in Kenya: an implementation science study.

accreditation is the most effective approach to ensure the quality of services. Laboratory performance can be evaluated using the World Health Organization (WHO)-SLIPTA checklist, which checks a laboratory´s compliance with ISO 15189 on a five-star score scale and improved using the SLMTA approach. Compliance is assessed by an external body and can result in accreditation. In this paper, we describe the steps taken by the Kenya Medical Research Institute (KEMRI) HIV Laboratory, Alupe, a resource-limited public entity, towards accreditation, and discuss the lessons learned. the laboratory adopted a SLMTA-SLIPTA approach that included targeted mentorship, on-site workshops, and training. Mentorship-based interventions were used to establish a robust quality management system. Targeted mentorship, on-site workshops, and training were conducted between September 2015 and July 2016. Audits used the SLIPTA checklist to detect gaps in 12 quality system essentials. Performance indicators including turnaround time, external quality assurance, sample rejection rates, and corrective actions were tracked. An external assessment by the national accreditation body was conducted between September 2016 and November 2016. training and mentorship-based interventions were successfully conducted. Quality management systems aligned with ISO 15189 were established. Baseline, midterm, and exit audits yielded scores of 47%, 75%, and 94% respectively. Early infant diagnosis external quality assurance scores were 100% in 2014-2016, while average viral load scores were at 60%, 70% and 90% during the same period. Turnaround time from September 2015 surpassed the 80% target. Accreditation was awarded in March 2017. the SLMTA-SLIPTA approach is suitable for quality improvement in resource-limited laboratories.

Performance of the RF Detectors of the Astroneu Array

Since 2014, the university campus of Hellenic Open University (HOU) has hosted the Astroneu array, which is dedicated to the detection of extensive air showers (EAS) induced by high-energy cosmic rays (CR). The Astroneu array incorporates 9 large particle scintillation detectors and 6 antennas sensitive to the radio frequency (RF) range 1–200 MHz. The detectors are adjusted in three autonomous stations operating in an environment with a strong electromagnetic background. As shown by previous studies, EAS radio detection in such environments is possible using innovative noise rejection methods, as well as advanced analysis techniques. In this work, we present the analysis of the collected radio data corresponding to an operational period of approximately four years. We present the performance of the Astroneu radio array in reconstructing the EAS axis direction using different RF detector geometrical layouts and a technique for the estimation of the shower core by comparing simulation and experimental data. Moreover, we measure the relative amplitudes of the two mechanisms that give rise to RF emission (the Askaryan effect and geomagnetic emission) and show that they are in good agreement with previous studies as well as with the simulation predictions.

Direct sampling with a step function

The direct sampling method proposed by Walker et al. (JCGS 2011) can generate draws from weighted distributions possibly having intractable normalizing constants. The method may be of interest as a tool in situations which require drawing from an unfamiliar distribution. However, the original algorithm can have difficulty producing draws in some situations. The present work restricts attention to a univariate setting where the weight function and base distribution of the weighted target density meet certain criteria. Here, a variant of the direct sampler is proposed which uses a step function to approximate the density of a particular augmented random variable on which the method is based. Knots for the step function can be placed strategically to ensure the approximation is close to the underlying density. Variates may then be generated reliably while largely avoiding the need for manual tuning or rejections. A rejection sampler based on the step function allows exact draws to be generated from the target with lower rejection probability in exchange for increased computation. Several applications of the proposed sampler illustrate the method: generating draws from the Conway-Maxwell Poisson distribution, a Gibbs sampler which draws the dependence parameter in a random effects model with conditional autoregression structure, and a Gibbs sampler which draws the degrees-of-freedom parameter in a regression with t-distributed errors.

Bayroot: Bayesian sampling of HIV-1 integration dates by root-to-tip regression.

The composition of the latent human immunodeficiency virus 1 (HIV-1) reservoir is shaped by when proviruses integrated into host genomes. These integration dates can be estimated by phylogenetic methods like root-to-tip (RTT) regression. However, RTT does not accommodate variation in the number of mutations over time, uncertainty in estimating the molecular clock, or the position of the root in the tree. To address these limitations, we implemented a Bayesian extension of RTT as an R package (bayroot), which enables the user to incorporate prior information about the time of infection and start of antiretroviral therapy. Taking an unrooted maximum likelihood tree as input, we use a Metropolis-Hastings algorithm to sample from the joint posterior distribution of three parameters (the rate of sequence evolution, i.e., molecular clock; the location of the root; and the time associated with the root). Next, we apply rejection sampling to this posterior sample of model parameters to simulate integration dates for HIV proviral sequences. To validate this method, we use the R package treeswithintrees (twt) to simulate time-scaled trees relating samples of actively and latently infected T cells from a single host. We find that bayroot yields significantly more accurate estimates of integration dates than conventional RTT under a range of model settings.

Multiple-point statistics and non-colocational soft data integration

In geostatistics, available conditional information is typically categorized as either hard (with no uncertainty) or soft (associated with an uncertainty) data. 2-point based Gaussian (kriging) simulation methods have since their inception been able to account for hard and soft data. In contrast, multiple-point statistical (MPS) simulation methods, that allow quantifying more complex phenomena than Gaussian methods, have focused mostly on conditioning to hard data. The most widely used method for accounting for soft data is to consider only co-located soft data. Here we define conditional information data through a probability distribution and show how a general probabilistic solution, in the form of a posterior probability distribution, can be obtained, combining conditional categorical information with information from a categorical training image. We demonstrate how to compute the conditional distribution, as needed by most MPS-based sequential simulation algorithms, that allows accounting for both co- and non-co-located hard and soft data. Examples are provided comparing the direct computation of conditional statistics to using more computational demanding rejection sampling. In order to reduce the significant computational demands, a distance-based measure is proposed to increase the entropy of conditional data, based on the distance to the point being estimated and simulated.

The association between peer rejection and aggression types: A meta-analysis

BackgroundAlthough previous studies have assessed the association between peer rejection and aggression, the results are mixed. ObjectiveThis article presents a meta-analysis of the association between peer rejection and aggression types (overt vs. relational) among children and adolescents. Participants and settingA total of 61 eligible studies with 70 independent effect sizes were included in the analysis (45,966 participants, Mage = 10.34, SD = 3.13). MethodsFirst, random-effects meta-analyses were conducted to explore the association between peer rejection and aggression types (overt vs. relational). Next, moderation analyses were conducted based on the Q statistics for categorical variables (culture, reporting method of peer rejection, reporting method of aggression) and the meta-regression analyses for continuous variable (age). ResultsPeer rejection was positively correlated with overall aggression (r = 0.42, 95 % CI [0.38, 0.47], p < 0.001), overt aggression (r = 0.46, 95 % CI [0.38, 0.54], p < 0.001) and relational aggression (r = 0.43, 95 % CI [0.35, 0.51], p < 0.001). This correlation was positive for each type when controlling for other form of aggression. Moderation analyses suggested that reporting method of aggression (self-report vs. peer-nomination vs. adult-report vs. observation), reporting method of peer rejection (self-report vs. peer-nomination vs. adult-report) and culture (collectivist vs. individualist) were moderators of the association between peer rejection and overall aggression. Culture moderated the association between peer rejection and overt aggression, while age moderated the association between peer rejection and relational aggression. ConclusionsThe findings showed a strong positive association between peer rejection and aggression, although this association varied by aggression type and other moderating variables.

Cooper-Frye sampling with short-range repulsion

This work incorporates the effect of short-range repulsion between particles into the Cooper-Frye hadron sampling procedure. This is achieved by means of a rejection sampling step, which prohibits any pair of particles from overlapping in the coordinate space, effectively modeling the effect of hard-core repulsion. The new procedure---called the fist sampler---is based on the package thermal-fist. It is used here to study the effect of excluded volume on cumulants of the (net-)proton number distribution in central collisions of heavy ions in a broad collision energy range in conjunction with exact global conservation of baryon number, electric charge, and strangeness. The results are compared with earlier calculations based on analytical approximations, quantifying the accuracy of the latter at different collision energies. An additional advantage of the new method over the analytic approaches is that it offers the flexibility provided by event generators, making it straightforwardly extendable to other observables.

Investigating the effect of depth of interaction on coincidence time resolution

Abstract The timing properties of scintillator-based detectors are a matter of importance in a wide range of fields. As fast scintillators progress and the sensors that preserve the quantized nature of the luminescent signal evolve, the optical transfer time may not always be negligible in ultra-fast time measurement. The present paper discusses the implications of this in a specific configuration for ultra-fast time measurement. To this end, we consider factors influencing the time spread such as the distribution of depth of interaction, non-instantaneous scintillation emission mechanism, and optical transport kinetics. Although such a topic has previously been studied by researchers, the aim of this work is focused on analyzing the detailed factors that govern arrival time spread. From these factors, we are able to obtain the post-interaction time spread, and we then derived the optical transfer time spread (OTTS) by a weighted sum of post-interaction time spread based on the cumulative density function of the depth of interaction. Based on the rejection sampling method, we could obtain the set of arrival times by the OTTS, and then the coincidence time resolution of radioactive sources was calculated. Consequently, we found that the difference in the attenuation coefficient causes the difference in the arrival time distribution, but it does not lend a significant contribution to the coincidence time resolution. In addition, when radiation with different energies is incident, the emission mechanism has a dominant effect on the time resolution, and thus incident radiation having higher energy in the same detector system has a higher light yield, which can exhibit better timing resolution compared to radiation with relatively low energy.

Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process

Abstract There are many models in the current statistical literature for making inferences based on samples selected from a finite population. Parametric models may be problematic because statistical inference is sensitive to parametric assumptions. The Dirichlet process (DP) prior is very flexible and determines the complexity of the model. It is indexed by two hyper-parameters: the baseline distribution and concentration parameter. We address two distinct problems in the article. Firstly, we review the current sampling methods for the concentration parameter, which use the continuous baseline distribution. We compare three different methods: the adaptive rejection method, the mixture of Gammas method and the grid method. We also propose a new method based on the ratio of uniforms. Secondly, in practice, some survey responses are known to be discrete. If a continuous distribution is adopted as the baseline distribution, the model is misspecified and standard inference may be invalid. We propose a discrete baseline approach to the DP prior and sample the unobserved responses from the finite population both using a Polya urn scheme and a Multinomial distribution. We applied our discrete baseline approach to a Phytophthora data set.

A new Autism Spectrum Disorder Discovery (ASDD) strategy using data mining techniques based on blood tests

Autism Spectrum Disorder (ASD) represents a heterogeneous developmental disability characterized by impairments in social communication and behavioral challenges. The problem of ASD begins with childhood and continues into adolescence as well as adulthood. The number of children suffering from ASD is constantly increasing, so a modern method must be found to make a quick and early diagnosis or discovery of ASD patients with high accuracy. Early discovery of ASD patients aims to quickly take the necessary measures with patients and take care of them until their speedy recovery. During this paper, a new Autism Spectrum Disorder Discovery (ASDD) strategy is provided to give a fast and delicate diagnosis for autism patients. The ASDD composes of two main layers, which are; (i) Pre-processing Layer (PL) and (ii) Autism Discovery Layer (ADL). In the PL, two approaches called feature selection and outlier rejection are used to filter the used data from any non-informative data before starting to train the diagnostic model in the ADL for providing delicate diagnosis. Hybrid Rejection Technique (HRT) as a new outlier rejection method is the main contribution of this paper. HRT includes two main stages called Quick Rejection Stage (QRS) as a fast stage and Precise Rejection Stage (PRS) as a delicate stage. QRS uses a standard deviation to quickly remove outliers and then PRS uses a Hybrid Bio-inspired Optimization Method (HBOM) that combines Binary Genetic Algorithm (BGA) and Binary Gray Wolf Optimizer (BGWO) to delicately eliminate the rest of outliers. According to PL in ASDD strategy, Fisher Score (FS) is applied to select the best subset of features and then HRT is used to reject bad training data. Finally, an Ensemble Technique (ET)that includes three classifiers called Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Deep Learning (DL) is used in ADL to diagnose ASD patients based on the filtered data from PL. The ASDD strategy has been compared to many modern strategies using ASD dataset that includes blood tests from children [1]. Experimental results proven that the ASDD strategy outperforms the other strategies in terms of many metrics called accuracy, error, sensitivity, precision, and execution-time with values 0.92, 0.08, 0.85, 0.80, and 2 s.

Perceived partial social integration, levels of distress and resilience, and COVID-19 vaccine rejection of Jewish and Arab citizens of Israel.

The present study examines the role of perceived partial social integration (PPSI) in determining the rejection of the COVID-19 vaccine of Jewish and Arab citizens of Israel. The research hypotheses are examined using a relatively large sample of the Israeli public, including 208 Arab and 600 Jewish adults, who have responded to an anonymous questionnaire pertaining, among other issues, to partial social integration and the individual level of vaccine uptake. Higher levels of PPSI were found to be associated with higher levels of vaccine rejection, in both Jewish and Arab samples. The Arab minority group regards themselves as less socially integrated into the Israeli society and therefore rejects the COVID-19 vaccine to a greater extent than the majority group. The Arab respondents expressed a higher level of psychological distress and a lower level of resilience compared with the Jewish participants. The perceived partial social integration score significantly predicted the levels of distress and resilience of the Jewish but not the Arab sample. The study indicates that increasing the vaccination rates depends more substantially on trust in the authorities than on leveraging greater pressure on individuals that reject the vaccine. Increased trust in the authorities and regarding oneself as an integral component of society are two vital conditions for vaccine acquiescence. Insufficient social integration is a major reason for vaccine rejection.

On error reduction by the symmetric rejection method in multi-stage biometric verification systems

On error reduction by the symmetric rejection method in multi-stage biometric verification systems

Results of using Luer-Lok access device for clinics, intensive care units, and emergency services with high pre-analytical errors: analysis of 491.850 samples

Abstract Objectives The effectiveness of the Luer-Lok™ Access Device (LL) with the intravenous catheter (IVC) on sample rejections, which is used to prevent primarily hemolysis in the emergency department (ED), clinics, and intensive care units (ICU), was examined. Methods A total of 491.850 samples of eight months from Amasya University Sabuncuoğlu Şerefeddin Training and Research Hospital were investigated retrospectively. Inpatient, intensive care unit and emergency department samples were included in the study. Pre- (BLL) and post-Luer Lok (ALL) rejection of the samples analyzed. In the BLL period, 3,463 rejection samples out of 253,818 (1.36%) in the September-December period of 2020; in the ALL period, 1819 rejected samples from 238,032 (0.76%) in January-April 2021 were analyzed for all reasons. Results It was determined that the use of LL significantly reduced all-cause sample rejections. In addition, a significant decrease was observed in the rates of hemolysis and clot-related rejection thanks to LL. Conclusions According to our study data, in patients with IVC with the LL device, the pressure brought by the syringe is mainly avoided, and reliable blood collection is achieved, similar to the routine vacutainer blood collection apparatus, and hemolysis- and clot-related sample rejections are largely prevented.

CSCE-Net: Channel-Spatial Contextual Enhancement Network for Robust Point Cloud Registration

Seeking reliable correspondences between two scenes is crucial for solving feature-based point cloud registration tasks. In this paper, we propose a novel outlier rejection network, called Channel-Spatial Contextual Enhancement Network (CSCE-Net), to obtain rich contextual information on correspondences, which can effectively remove outliers and improve the accuracy of point cloud registration. To be specific, we design a novel channel-spatial contextual (CSC) block, which is mainly composed of the Channel-Spatial Attention (CSA) layer and the Nonlocal Channel-Spatial Attention (Nonlocal CSA) layer. The CSC block is able to obtain more reliable contextual information, in which the CSA layer can selectively aggregate the mutual information between the channel and spatial dimensions. The Nonlocal CSA layer can compute feature similarity and spatial consistency for each correspondence, and the CSA layer and Nonlocal CSA layer can support each other. In addition, to improve the distinguishing ability between inliers and outliers, we present an advanced seed selection mechanism to select more dependable initial correspondences. Extensive experiments demonstrate that CSCE-Net outperforms state-of-the-art methods for outlier rejection and pose estimation tasks on public datasets with varying 3D local descriptors. In addition, the network parameters of CSCE-Net are reduced from 1.05M to 0.56M compared to the recently learning-based outlier rejection method PointDSC.

Identification of Nonlinear State-Space Systems With Skewed Measurement Noises

In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student’s t (GHSkewt) distribution is employed to describe the skewed noises and formulate the hierarchical model of the considered system. A unified framework for estimating unknown states and model parameters is presented based on expectation-maximization (EM) algorithm, in which the forward filtering backward simulation with rejection sampling (RS-FFBSi) is employed to efficiently estimate the smoothing densities of the hidden states, and optimization method is adopted to update model parameters. One numerical study and the electro-mechanical positioning system (EMPS) are employed to verify the effectiveness of the developed approach.

Loading a relativistic Kappa distribution in particle simulations

A procedure for loading particle velocities from a relativistic kappa distribution in particle-in-cell and Monte Carlo simulations is presented. It is based on the rejection method and the beta prime distribution. The rejection part extends earlier method for the Maxwell–Jüttner distribution, and then the acceptance rate reaches ≳95%. Utilizing the generalized beta prime distributions, we successfully reproduce the relativistic kappa distribution, including the power-law tail. The derivation of the procedure, mathematical preparations, comparison with other procedures, and numerical tests are presented.

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fluid–fluid/ fluid–solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance–rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure.

High Throughput Implementation of Post-Quantum Key Encapsulation and Decapsulation on GPU for Internet of Things Applications

Internet of Things (IoT) sensor nodes are placed ubiquitously to collect information, which is then vulnerable to malicious attacks. For instance, adversaries can perform side channel attack on the sensor nodes to recover the symmetric key for encrypting IoT data. Refreshing the symmetric key frequently can reduce the risk of compromised keys. However, the number of sensor nodes connected to the gateway and cloud server is massive. Refreshed symmetric keys need to be sent to gateway devices and cloud server frequently with a secure key encapsulation mechanism (KEM), which is time-consuming. In this article, novel and efficient implementation techniques are proposed to accelerate Kyber, a post-quantum KEM, on a Graphics Processing Unit (GPU). Fully parallel implementation of number theoretic transform (NTT) with combined levels is presented, which is 2.65× faster than state-of-the-art result on a GPU. Other proposed techniques include parallel rejection sampling, central binomial distribution with coalesced memory access and parallel fine-grain AES-256. These techniques enable high throughput performance with 162760 encapsulations/second and 107631 decapsulations/second on an RTX2060 GPU. This is also the first fine grain implementation of post-quantum KEM (Kyber) on a GPU, which can be used to offer key encapsulation/decapsulation as a service to reduce the burden on IoT systems.