Filtering Approach Research Articles

Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

The online version contains supplementary material available at 10.1007/s00181-022-02356-9.

Fuzzy-model-based ℋ∞ filtering for discrete-time singular Markov jump nonlinear systems against hybrid attacks

This paper investigates the H ∞ filtering problem for a class of discrete-time singular Markov jump nonlinear systems against hybrid attacks via a fuzzy-model-based method, in which the hybrid attacks contain deception attacks and denial of service attacks. Two random variables subject to the Bernoulli distribution are used to describe whether the hybrid attacks are encountered during the measurement output of the original system being transmitted to the filter. By using linear matrix inequality technology and Lyapunov stability theory, some sufficient conditions are given to guarantee the considered systems are stochastically admissible and meet H ∞ performance index γ. The design approach of the secure filter and a specific form to acquire the expected secure filter gains are obtained, respectively. Finally, to demonstrate the effectiveness of the proposed approach, both a numerical example and a practical example are provided.

Vehicle trajectory reconstruction for intersections: an integrated wavelet transform and Savitzky-Golay filter approach

Reconstructing vehicle trajectories is an essential step in obtaining high-precision vehicle trajectory data owing to observation errors. Although considerable efforts have been made to reconstruct trajectories on road segments, it is still challenging to reconstruct two-dimensional vehicle trajectories at intersections. In this study, a three-step vehicle trajectory reconstruction method for intersections was proposed to correct abnormal velocities and direction angles in raw trajectories. First, outliers were identified by wavelet transform. They were then filtered out and interpolated using Gaussian kernel-based locally weighted linear regression. Finally, the trajectories were smoothed using a Savitzky-Golay filter. The proposed method was validated using empirical trajectory data collected at 15 intersections in Shanghai, China. The results demonstrate that the proposed trajectory reconstruction method performs well in terms of trajectory rationality and consistency. The mean outlier rate and root mean square error of the reconstructed trajectories are 3% and 0.821 m, respectively.

A modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting

Sifting through features to find the most related ones is known as feature selection. This paper introduces a feature-selection technique based on a modified approach in order to improve classification performance using fewer features: the chi-squared with recursive feature elimination (χ2-RFE) method. It combines χ2 as a filter approach with recursive feature elimination as a wrapper approach. The algorithm developed for the χ2-RFE method is superior to six other algorithms in measures of average performance, with acceptable computing time. This is demonstrated by application to a data set of Chinese listed companies with a sample size of 47 172 and 535 characteristics, and the efficacy of the χ2-RFE algorithm is further confirmed by an experiment on a German data set with a sample size of 1000 and 24 characteristics. Since it can be challenging to achieve high accuracy and good performance in measures related to imbalanced data with only a few features, we extensively analyze the potential of our modified feature-selection framework, χ2-RFE, to provide a solution.

A HYBRID SOLVER FOR THE RADIATIVE TRANSFER EQUATION IN NONGRAY COMBUSTION GASES

The discrete ordinates method (DOM) or its variant, the finite angle method (FAM), is a popular solution method for the radiative transfer equation (RTE). Accurate solution to the RTE using the DOM or FAM requires many solid angles (directions) in multidimensional geometry. In combustion gases, where the absorption coefficient oscillates wildly and the RTE must be solved repeatedly, this method becomes computationally intractable. Here, the FAM is hybridized with the P<sub>1</sub> approximation, which is efficient since it requires solution to a single partial differential equation as opposed to a set of directional RTEs in the FAM. The P<sub>1</sub> approximation is accurate when the intensity field is fairly isotropic, as evidenced in optically thick media. Hence, the hybridization employs the FAM for optically thin and intermediate spectral intervals and employs the P<sub>1</sub> approximation for optically thick spectral intervals. The objective of the present study is to determine optimal parameters for hybridization that can provide the best compromise between accuracy and efficiency. Using a narrowband-based box model for carbon dioxide and water vapor, the nongray radiative transfer equation is solved in media with nonuniform properties enclosed in multidimensional enclosures. Two different approaches-cutoff and filter optical thickness-are investigated for hybridizing. Several problems, both two- and three-dimensional, and with and without coupling to other modes of heat transfer are considered. The filter approach was found to be the best choice for prediction of the radiative source and temperature (in the case of a coupled mode), while the cutoff approach was found to be the best for prediction of wall radiative heat fluxes.

A Novel Intrusion Detection Framework (IDF) using Machine Learning Methods

An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to identify the key elements for recognizing DPM intrusion attempts. An Improved Particle Swarm Optimization is used (IPSO). IPSO is a hybrid method that uses both filter and wrapper approaches to generate accurate and relevant information for the classification step that follows. Primary Selection Phase (PSP) and Completed Selection Phase (CSP) are the two consecutive feature selection phases in IPSO. PSP employs a filtering approaches to quickly identify the most significant features for detecting intrusion threats while eliminating those that are redundant or ineffective. In CSP, the next level of IPSO, this behavior reduces the computing cost. For accurate feature selection, CSP uses Binary Particle Swarm Optimization (Bi-PSO) as a wrapper approach. Based on the most effective features identified by FSM, The CM aims to identify intrusion attempts with the minimal processing time. Therefore, a K-Nearest Neighbor KNN classifier has been deployed. As a result, based on the significant features identified by the IPSO technique, KNN can accurately detect intrusion attacks with the least amount of processing time. The experimental results have shown that the proposed IDF outperforms other recent techniques using UNSW_NB-15 dataset. The accuracy, precision, recall, F1score, and processing time of the experimental outcomes of our findings were assessed. Our results were competitive with an accuracy of 99.8%, precision of 99.94%, recall of 99.85%, F1-score of 99.89%, and excursion time of 59.15s when compared to the findings of the current works.

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks.

Multi-Parameter Adaptive Notch Filter (MPANF) for Enhanced Interference Mitigation

<h3>Abstract</h3> Interference signals degrade global navigation satellite system (GNSS) performance and must be mitigated. Chirp signals can be mitigated with an adaptive notch filter (ANF), but the dynamic behavior limits performance. An ANF determines the instantaneous frequency and removes interference with a notch filter. However, there are several limitations. In this article, we propose a multi-parameter adaptive notch filter (MPANF) approach that significantly enhances conventional ANFs. First, it uses an loop-bandwidth control algorithm (LBCA) to alter the loop bandwidth of an frequency-locked loop (FLL)-based adaptation algorithm to facilitate superior tracking agility-to-precision trade-off. Second, it dynamically adjusts the notch depth to switch on interference mitigation or pass the signal through. Third, it modifies the notch width to accommodate tracking stability and optimize interference signal suppression to GNSS signal removal. The presented MPANF exhibits superior performance against chirp signals, including faster response to jump discontinuities.

Developed AC/DC/AC Converter Structure Based on Shunt Active Filter and Advanced Modulation Approach for Asymmetrical Cascade H-Bridge Multilevel Inverters

Developed AC/DC/AC Converter Structure Based on Shunt Active Filter and Advanced Modulation Approach for Asymmetrical Cascade H-Bridge Multilevel Inverters

Barrier Lyapunov functions-based adaptive neural tracking control for non-strict feedback stochastic nonlinear systems with full-state constraints: A command filter approach

&lt;p style='text-indent:20px;'&gt;In this paper, an adaptive neural network command filter controller is investigated for a class of non-strict feedback stochastic nonlinear systems with full-state constraints. By using the command filter approach and error compensation mechanism, the "explosion of complexity" problem caused by the backstepping method and the filtering errors are eliminated. In order to avoid excessive and burdensome computations and to ensure that the backstepping method works normally for non-strict feedback structures, neural networks are employed to approximate the unknown nonlinear functions that contain all the state variables of the system. Meanwhile, the barrier Lyapunov functions are constructed to ensure the constraints are not transgressed. Finally, based on the Lyapunov stability theorem, an adaptive neural tracking controller is presented to guarantee that all the signals of the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) in probability, and the tracking error converges to a small neighborhood around the origin, besides the full-state constraints are not violated. The simulation results are given to confirm the effectiveness of the proposed control method.&lt;/p&gt;

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.

Extended Kalman filter for fish weight estimation using augmented fish population growth model*

The fish weight, which indicates the growth rate of fish, is a crucial factor that optimizes the feeding rate and shapes the life history of fish in recirculating aquaculture systems. Estimating or predicting fish weight is essential for achieving better control of the output of an aquaculture system. However, many difficulties arise from the lack of direct measurement of fish weight. Currently, aquaculture sensors that directly estimate the fish's weight are unavailable. Additionally, the fish growth model that relies on the fish weight is affected by various environmental factors like water temperature, dissolved oxygen, salinity, and light, increasing the difficulty in estimating the weight. This paper proposes an extended Kalman filter approach for assessing the fish weight from directly available measurements of the fish stocking density and dissolved oxygen. The extended Kalman approach relies on a new version of the fish population growth model, which includes the fish population and dissolved oxygen dynamics. The simulation results demonstrate that the extended Kalman filter algorithm achieves good weight estimation in the nominal case and the presence of process and measurement noises.

Adaptive Optimal Control of Faulty Nonlinear DC Microgrids With Constant Power Loads: Dual-Extended Kalman Filter Approach

This article investigates the problem of estimating actuator fault and states and controlling the bus voltage in direct current microgrids (DC MGs) with linear and nonlinear constant power loads (CPLs). It is considered that the DC MG states are not fully measurable and the utilized sensors are not ideal and noisy. Additionally, the actuator fault occurs and it is modeled as an additive term in the power system dynamics. These issues, including nonlinearities, un-measurable states, noisy measures, and actuator fault indispensably degrade the operation of the DC MG. To solve this issue, initially, a dual-extended Kalman filter (dual-EKF) is suggested for the fault and state estimation. It decomposes the process of estimating the state and actuator fault to reduce the online computational burden. For the control purpose, a linear parameter varying (LPV) model predictive control (MPC) is suggested to regulate the current and voltage of the DC MG. It benefits the nonlinear system modeling of LPV representation and constrained-based design procedure of the MPC to result in an accurate and low online computational burden dealing with system constraints. By deploying the overall robust adaptive dual-EKF estimation-based LPV-MPC, there is no need to have any prior knowledge of all system states and actuator faults in prior. The theoretical analysis and controller design are validated by numerical simulations on a typical islanded DC MG and comparisons are done with state-of-the-art estimation and control strategies.

A Nonlinear Filter by Pseudo-Formal Linearization for Nonlinear Time-Varying Systems

This paper is concerned with a nonlinear filter for a general class of nonlinear time-varying systems, by making use of a pseudo-formal linearization method and Kalman filter approach. The given nonlinear time-varying dynamic and measurement equations are piecewisely linearized by the formal linearization method using Taylor expansion so that Kalman filter can be applied for each linearized time-varying equations. Then the resulting filters are smoothly united into a single nonlinear filter by an automatic choosing function. The efficiency of this filter is shown through numerical examples.

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Random access schemes are widely used in IoT wireless access networks. They enable a reduced complexity and overcome power consumption constraints. Nevertheless, random access results in high packet losses which are caused by overlapping transmissions. Signal detection methods for digital modulation techniques are typically based on the well-established <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matched filter</i> , which is proven as the optimal filter under additive white Gaussian noise for minimizing error probability. However, with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">colored</i> interference arising from the overlapping IoT transmissions, deep learning approaches are being considered a suitable alternative. In this paper, we present a hybrid framework, dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybNet</i> , that alternates between deep learning and match filter pathways based on the perceived interference level. This helps the detector to work in a broader range of conditions, optimally leveraging both the matched filter and deep learning advantages. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that leveraging domain knowledge by pre-processing the input data in conjunction with the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.

A methodology to estimate the optimal debt ratio when asset returns, and default probability follow stochastic processes

The purpose of this paper is to propose a methodology that allies both theoretical and empirical aspects to model and solve the problem of finding the optimal market-based corporate financial structure in a risky environment; the firm's asset value follows a geometric Brownian motion with a return adjusted by the probability of default which is stochastic and follows an Ornstein-Uhlenbeck process. We consider a decision manager to optimize the financial structure of his company, maximizes the expected utility of the shareholders' final wealth by solving a dynamic programming problem. The value function obeys a quadratic Hamilton-Jacobi-Bellman (HJB) equation and the explicit solution gives the optimal debt ratio. The Kalman filter approach is used to estimate the model parameters. We empirically examined the implications of the credit ratings on the optimal capital structure decision. The data used are daily, obtained from the 'Data Stream' database and cover a sample of 16 U.S companies of various sectors and different rating categories over the period from January 01, 2015, to December 31, 2015. The estimated average optimal debt ratios are relatively high showing different patterns between and within investment and speculative-grade firms. In fact, the evolution of the optimal debt ratio according to the rating is influenced by a restrictive or an easy entry into the debt market and by the value of the estimated rate of return.

Hybrid Meta-Heuristic based Feature Selection Mechanism for Cyber-Attack Detection in IoT-enabled Networks

Today's technologically advanced connected world is mostly reliant on the Internet of Things (IoT)-enabled smart gadgets and easy connectivity. These smart gadgets are more susceptible to malicious practices found in network traffic, which is one of the biggest challenges in cyber security domain. As a result, many systems and end-users are adversely affected by this practice. However, intrusion detection systems (IDS) are often applied to guard against cyber-attacks. Since, IDS plays a key role in detecting and preventing cyber-attacks in IoT-enabled networks, but design of an efficient and fast IDS system for cyber-attack detection is still a challenging research issue. Moreover, IDS datasets contain multiple features and to design an efficient and fast IDS, feature selection (FS) is an essential mechanism to remove the irrelevant and redundant features from large IDS datasets. Thus, this paper has proposed a hybrid feature selection scheme in which statistical test-based filter approaches such as Chi-Square (χ^2), Pearson's Correlation Coefficient (PCC), and Mutual Information (MI) are combined with a Non-Dominated Sorting Genetic Algorithm (NSGA-II)-based metaheuristic approach for optimization of features. In the proposed scheme, filter-based methods are employed to rank the features for guided population initialization in NSGA-II for faster convergence towards a solution. Performance evaluation of the proposed scheme is evaluated using the ToN-IoT dataset in terms of number of selected features and accuracy. Experimental outcomes are compared with some latest state-of-art techniques. Result analysis confirms the superior performance of the proposed scheme with minimum number of optimized features (only 13 out of 43 features) and maximum accuracy (99.48%).

Robust Kalman Filter for Position Estimation of Automated Guided Vehicles Under Cyberattacks

Automated Guided Vehicles (AGVs) have become a key part of many industries where they handle the task of managing material flows. As a result, they are very important targets for cyberattacks to cripple organizations by using methods such as False Data Injection (FDI) and Denial of Service (DoS) on sensors and measurement data. This paper introduces a new robust Kalman Filter (KF) for the AGV state estimation using Huber Loss Function. A statistical method known as M-estimation is used to solve the regression issue robustly and establish the equivalence between the Kalman filter and a specific least squares regression problem. For adaptive estimation of the unknown a priori state and observation noise statistics concurrently with the system states, M-robust estimators are developed. The proposed method tackles the state estimation issue against different kinds of cyber-attacks such as pulse, ramp, and DoS cyberattacks. The position estimation using constant velocity tracking based on KF employing with and without the robustification methods are compared. The results confirm the effectiveness and the robustness of the proposed Filter approach against the FDI due to the cyber-attacks compared with traditional Filters. Furthermore, the proposed method is investigated practically with Adlink-ROS AGV.

6D Object Pose Estimation Using a Particle Filter With Better Initialization

Estimation of 6D object poses is a key issue in robotic grasping tasks. Recently, many high-performance learning-based methods have been introduced using robust deep learning techniques; however, applying these methods to real robot environments requires many ground truth 6D pose annotations for training. To address this problem, we propose a template matching-based particle filter approach for 6D pose estimation; the proposed method does not require ground truth 6D poses. Although particle filter approaches can stochastically avoid local optima, they require adequate initial pose hypotheses for estimating an accurate 6D object pose. Therefore, we estimated an initial translation of the target object for accurately initializing a particle filter by developing a new deep network. Once the proposed centroid prediction network (CPN) is trained with a specific dataset, no additional training is required for new objects not in the dataset. We evaluated the performance of the CPN and the proposed 6D pose estimation method on benchmark datasets, which demonstrated that the CPN can predict the centroid for any object, including those not in the training data, and that our 6D pose estimation method outperforms existing methods for partially occluded objects. Finally, we tested a grasping task based on our proposed method using a real robot platform to demonstrate an application of our method to a downstream task. This experiment shows that our method can be applied to part assembly, bin picking, and object manipulation without large training datasets with 6D pose annotations. The code and models are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/oorrppp2/Particle_filter_approach_6D_pose_estimation</uri> .

Exploring Conflicts in Whole Genome Phylogenetics: A Case Study Within Manakins (Aves: Pipridae).

Some phylogenetic problems remain unresolved even when large amounts of sequence data are analyzed and methods that accommodate processes such as incomplete lineage sorting are employed. In addition to investigating biological sources of phylogenetic incongruence, it is also important to reduce noise in the phylogenomic dataset by using appropriate filtering approach that addresses gene tree estimation errors. We present the results of a case study in manakins, focusing on the very difficult clade comprising the genera Antilophia and Chiroxiphia. Previous studies suggest that Antilophia is nested within Chiroxiphia, though relationships among Antilophia+Chiroxiphia species have been highly unstable. We extracted more than 11,000 loci (ultra-conserved elements and introns) from whole genomes and conducted analyses using concatenation and multispecies coalescent methods. Topologies resulting from analyses using all loci differed depending on the data type and analytical method, with 2 clades (Antilophia+Chiroxiphia and Manacus+Pipra+Machaeopterus) in the manakin tree showing incongruent results. We hypothesized that gene trees that conflicted with a long coalescent branch (e.g., the branch uniting Antilophia+Chiroxiphia) might be enriched for cases of gene tree estimation error, so we conducted analyses that either constrained those gene trees to include monophyly of Antilophia+Chiroxiphia or excluded these loci. While constraining trees reduced some incongruence, excluding the trees led to completely congruent species trees, regardless of the data type or model of sequence evolution used. We found that a suite of gene metrics (most importantly the number of informative sites and likelihood of intralocus recombination) collectively explained the loci that resulted in non-monophyly of Antilophia+Chiroxiphia. We also found evidence for introgression that may have contributed to the discordant topologies we observe in Antilophia+Chiroxiphia and led to deviations from expectations given the multispecies coalescent model. Our study highlights the importance of identifying factors that can obscure phylogenetic signal when dealing with recalcitrant phylogenetic problems, such as gene tree estimation error, incomplete lineage sorting, and reticulation events. [Birds; c-gene; data type; gene estimation error; model fit; multispecies coalescent; phylogenomics; reticulation].