scholarly journals Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2991
Author(s):  
Kailai Li ◽  
Florian Pfaff ◽  
Uwe D. Hanebeck
Keyword(s):  
Particle Filter ◽  
State Of The Art ◽  
Numerical Results ◽  
Superior Performance ◽  
Strong Nonlinearity ◽  
Underlying Distribution ◽  
Fisher Distribution ◽  
Deterministic Sampling ◽  
Von Mises ◽  
Sampling Approach

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.

An evolutionary based framework for many-objective optimization problems

2018 ◽  
Vol 35 (4) ◽  
pp. 1805-1828 ◽  
Author(s):  
Kimia Bazargan Lari ◽  
Ali Hamzeh
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Population Diversity ◽  
Numerical Results ◽  
Superior Performance ◽  
High Dimensional ◽  
Test Problems ◽  
Content Type ◽  
Objective Space

Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon. Design/methodology/approach To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms. Findings The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark. Originality/value This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

scholarly journals Utilization of Eco-Friendly Waste Generated Nanomaterials in Water-Based Drilling Fluids; State of the Art Review

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4171
Author(s):  
Rabia Ikram ◽  
Badrul Mohamed Jan ◽  
Akhmal Sidek ◽  
George Kenanakis
Keyword(s):  
State Of The Art ◽  
Superior Performance ◽  
Future Research ◽  
Drilling Fluids ◽  
Drill Cuttings ◽  
Environmental Friendly ◽  
Water Based ◽  
Drilling Operations ◽  
Filtration Properties

An important aspect of hydrocarbon drilling is the usage of drilling fluids, which remove drill cuttings and stabilize the wellbore to provide better filtration. To stabilize these properties, several additives are used in drilling fluids that provide satisfactory rheological and filtration properties. However, commonly used additives are environmentally hazardous; when drilling fluids are disposed after drilling operations, they are discarded with the drill cuttings and additives into water sources and causes unwanted pollution. Therefore, these additives should be substituted with additives that are environmental friendly and provide superior performance. In this regard, biodegradable additives are required for future research. This review investigates the role of various bio-wastes as potential additives to be used in water-based drilling fluids. Furthermore, utilization of these waste-derived nanomaterials is summarized for rheology and lubricity tests. Finally, sufficient rheological and filtration examinations were carried out on water-based drilling fluids to evaluate the effect of wastes as additives on the performance of drilling fluids.

scholarly journals Active Learning in the Geometric Block Model

2020 ◽  
Vol 34 (04) ◽  
pp. 3641-3648 ◽  
Author(s):  
Eli Chien ◽  
Antonia Tulino ◽  
Jaime Llorca
Keyword(s):  
Active Learning ◽  
Community Detection ◽  
Random Graphs ◽  
State Of The Art ◽  
Superior Performance ◽  
Model Parameters ◽  
Block Model ◽  
Stochastic Block Model ◽  
Synthetic Datasets ◽  
Community Detection Problems

The geometric block model is a recently proposed generative model for random graphs that is able to capture the inherent geometric properties of many community detection problems, providing more accurate characterizations of practical community structures compared with the popular stochastic block model. Galhotra et al. recently proposed a motif-counting algorithm for unsupervised community detection in the geometric block model that is proved to be near-optimal. They also characterized the regimes of the model parameters for which the proposed algorithm can achieve exact recovery. In this work, we initiate the study of active learning in the geometric block model. That is, we are interested in the problem of exactly recovering the community structure of random graphs following the geometric block model under arbitrary model parameters, by possibly querying the labels of a limited number of chosen nodes. We propose two active learning algorithms that combine the use of motif-counting with two different label query policies. Our main contribution is to show that sampling the labels of a vanishingly small fraction of nodes (sub-linear in the total number of nodes) is sufficient to achieve exact recovery in the regimes under which the state-of-the-art unsupervised method fails. We validate the superior performance of our algorithms via numerical simulations on both real and synthetic datasets.

WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Qi Xin ◽  
Shaohao Hu ◽  
Shuaiqi Liu ◽  
Ling Zhao ◽  
Shuihua Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Traumatic Injury ◽  
Recurrence Plot ◽  
Superior Performance ◽  
Eeg Classification ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

scholarly journals DeepTable: a permutation invariant neural network for table orientation classification

2020 ◽  
Vol 34 (6) ◽  
pp. 1963-1983
Author(s):  
Maryam Habibi ◽  
Johannes Starlinger ◽  
Ulf Leser
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Superior Performance ◽  
Web Pages ◽  
Specific Information ◽  
Silver Standard ◽  
Present Information ◽  
Previous State ◽  
Novel Method

Abstract Tables are a common way to present information in an intuitive and concise manner. They are used extensively in media such as scientific articles or web pages. Automatically analyzing the content of tables bears special challenges. One of the most basic tasks is determination of the orientation of a table: In column tables, columns represent one entity with the different attribute values present in the different rows; row tables are vice versa, and matrix tables give information on pairs of entities. In this paper, we address the problem of classifying a given table into one of the three layouts horizontal (for row tables), vertical (for column tables), and matrix. We describe DeepTable, a novel method based on deep neural networks designed for learning from sets. Contrary to previous state-of-the-art methods, this basis makes DeepTable invariant to the permutation of rows or columns, which is a highly desirable property as in most tables the order of rows and columns does not carry specific information. We evaluate our method using a silver standard corpus of 5500 tables extracted from biomedical articles where the layout was determined heuristically. DeepTable outperforms previous methods in both precision and recall on our corpus. In a second evaluation, we manually labeled a corpus of 300 tables and were able to confirm DeepTable to reach superior performance in the table layout classification task. The codes and resources introduced here are available at https://github.com/Marhabibi/DeepTable.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals A Novel EM Implementation for Initial Alignment of SINS Based on Particle Filter and Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yanbing Guo ◽  
Lingjuan Miao ◽  
Yusen Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Particle Swarm ◽  
The State ◽  
Nonlinear Filters ◽  
Measurement Noise ◽  
Strong Nonlinearity ◽  
Initial Alignment ◽  
Swarm Optimization ◽  
Noise Parameters

For nonlinear systems in which the measurement noise parameters vary over time, adaptive nonlinear filters can be applied to precisely estimate the states of systems. The expectation maximization (EM) algorithm, which alternately takes an expectation- (E-) step and a maximization- (M-) step, has been proposed to construct a theoretical framework for the adaptive nonlinear filters. Previous adaptive nonlinear filters based on the EM employ analytical algorithms to develop the two steps, but they cannot achieve high filtering accuracy because the strong nonlinearity of systems may invalidate the Gaussian assumption of the state distribution. In this paper, we propose an EM-based adaptive nonlinear filter APF to solve this problem. In the E-step, an improved particle filter PF_new is proposed based on the Gaussian sum approximation (GSA) and the Monte Carlo Markov chain (MCMC) to achieve the state estimation. In the M-step, the particle swarm optimization (PSO) is applied to estimate the measurement noise parameters. The performances of the proposed algorithm are illustrated in the simulations with Lorenz 63 model and in a semiphysical experiment of the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles.

scholarly journals Robust place recognition based on salient landmarks screening and convolutional neural network features

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096696
Author(s):  
Jie Niu ◽  
Kun Qian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Environmental Modeling ◽  
Superior Performance ◽  
Similarity Measurement ◽  
Natural Environments ◽  
Place Recognition ◽  
Average Precision ◽  
Specific Object

In this work, we propose a robust place recognition measurement in natural environments based on salient landmark screening and convolutional neural network (CNN) features. First, the salient objects in the image are segmented as candidate landmarks. Then, a category screening network is designed to remove specific object types that are not suitable for environmental modeling. Finally, a three-layer CNN is used to get highly representative features of the salient landmarks. In the similarity measurement, a Siamese network is chosen to calculate the similarity between images. Experiments were conducted on three challenging benchmark place recognition datasets and superior performance was achieved compared to other state-of-the-art methods, including FABMAP, SeqSLAM, SeqCNNSLAM, and PlaceCNN. Our method obtains the best results on the precision–recall curves, and the average precision reaches 78.43%, which is the best of the comparison methods. This demonstrates that the CNN features on the screened salient landmarks can be against a strong viewpoint and condition variations.

scholarly journals CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Aboubakar Nasser Samatin Njikam ◽  
Huan Zhao
Keyword(s):  
Text Classification ◽  
Building Block ◽  
Large Scale ◽  
State Of The Art ◽  
Building Blocks ◽  
Training Data ◽  
Superior Performance ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Convolutional Network

This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems. This new architecture is composed of four building blocks for feature extraction. Each of these building blocks, except the last one, uses 1 × 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block. In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block. Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net’s superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.

Sign in / Sign up

Export Citation Format

Share Document