Numerical Analysis and Scientific Computing Commons^™

Optimized Linear Adrc Control For A Class Of Fractional-Order Chaotic Systems, Huang Yu, Xie Tian, Wu Rui

Journal of System Simulation

Abstract: Linear active disturbance rejection control has many parameters and it is difficult to obtain appropriate parameters. Therefore, a quantum-behaved particle swarm optimization algorithm based on cosine decreasing function is proposed in this article for searching optimal LADRC parameters. In this algorithm, cosine decreasing function and updated equation of quantum-behaved particle swarm optimization algorithm are combined. By using the variable characteristic of cosine decreasing function with the increase of iteration times, the original global optimization ability of quantum particle swarm optimization is retained and its poor local optimization ability is overcome. And the simulation results show that the LADRC optimized …

Gcps Adaptive Scheduling Model Based On Cooperative Executor, Zhang Jing, Chen Yao, Sun Jun, Hongbo Fan

Journal of System Simulation

Abstract: Aiming at the problem that the uncertainty of grid cyber physical systems leads to chain failure, an adaptive GCPS dispatching model based on co-actuator is established. First, the constraint conditions of the system are analyzed. A model constraints of GCPS system is presented to describe the constraint conditions of the power system, and it is proved that it meets the consistency of the measure and representing methods. Second, the optimal value of approximation error is solved by PILOT, and the framework of CA-SADM is described. Finally, the performance index, output power accuracy and the influence of fault on the …

Simulation Of Axial Vibration Characteristics Of Marine Riser Under Suspension Mode, Chuan Wang, Li Jun, Zhenqiang Xie, Guorong Wang

Journal of System Simulation

Abstract: The mechanical simulation model of vertical vibration of Marine riser under suspension mode has been established based on the basic principle of dynamics theory. In the model, the influence of heave movement and top tensioner is considered. The vibration equation is solved by the finite difference method to obtain the distribution of axial stress and displacement. Besides, the effects of suspension mode, buoyancy block,water depth, wall thickness and LMRP on the vertical vibration of riser are discussed. According to the results, the soft suspension mode is superior to the hard suspension mode. The buoyancy blocks will increase the …

Simulation Of The Resistance Of Superhydrophobic Microchannel In Turbulent Regime, Chunxi Li, Qingze Cui, Xuemin Ye

Journal of System Simulation

Abstract: VOF model and the Realizable k-ε turbulent model are used to simulate the three-dimensional flow field of superhydrophobic microchannels with longitudinal and transverse microstructures in turbulent regime, the effects of structural parameters on the resistance characteristics of superhydrophobic surface are analyzed. The results show that the superhydrophobic microchannels with longitudinal microgrooves exhibit significant reductions in drag; with the increase of the free shear area ratio and the width of the microchannel, the ratio of pressure drop increases, and the average friction factor decreases, as well as the drag reduction effect is highly significant; with the increase of …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …

A Data Analysis Of The World Happiness Index And Its Relation To The North-South Divide, Charles Alba

Undergraduate Economic Review

In this document, we perform a detailed data analysis on the World Happiness Report with its relation to the socio-economic North-South Divide. In order to do so, we perform some extensive data cleaning and analysis before querying on the World Happiness Report. Our results based on Hypothesis Testing determines the happiness of the Global North is greater than that of the Global South. Furthermore, our queries show that the mean happiness score for the Global North significantly outweighing that of the South. Likewise, the 10 'Happiest' nations all belong to the Global North whereas the 10 'least happy' nations belong …

Incorporating Word Order Explicitly In Glove Word Embedding, Brandon Cox

Computer Science and Computer Engineering Undergraduate Honors Theses

Word embedding is the process of representing words from a corpus of text as real number vectors. These vectors are often derived from frequency statistics from the source corpus. In the GloVe model as proposed by Pennington et al., these vectors are generated using a word-word cooccurrence matrix. However, the GloVe model fails to explicitly take into account the order in which words appear within the contexts of other words. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. The most successful method involves directly concatenating several word vector matrices for each position in …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

The R Journal (December 2019) 11(2): Complete Issue, The R Foundation

The R Journal

Editorial, Michael J. Kane

Contributed Research Articles

Using Web Services to Work with Geodata in R, Jan-Philipp Kolb

orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization, Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, and Xin Zhang

coxed: An R Package for Computing Duration-Based Quantities from the Cox Proportional Hazards Model, Jonathan Kropko and Jeffrey J. Harden

Modeling Regimes with Extremes: The Bayesdfa Package for Identifying and Forecasting Common Trends and Anomalies in Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, and Michael A. Litzow

Fitting Tails by the Empirical Residual …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2019-09-05 and 2020-02-24.

Conference: Report Conectar 2019, Marcela Alfaro Córdoba, Frans Van Dunné, Agustín Gómez Meléndez, Jacob Van Etten

The R Journal

ConectaR 2019: Encuentro de Usuarios R en Latinoamérica, took place during January 24-26, 2019 at the University of Costa Rica, in San José, Costa Rica. It was the first event in Central America endorsed by The R Foundation, and it was held completely in Spanish. The majority of the attendants were from Costa Rica (85%), but we had participants from 12 countries: Costa Rica, Guatemala, Peru, Colombia, Mexico, Argentina, Uruguay, Chile, Spain, the Netherlands, France and the USA. The three-day event consisted of talks, workshops, and poster sessions.

Lpirfs: An R Package To Estimate Impulse Response Functions By Local Projections, Philipp Adämmer

The R Journal

Impulse response analysis is a cornerstone in applied (macro-)econometrics. Estimating impulse response functions using local projections (LPs) has become an appealing alternative to the traditional structural vector autoregressive (SVAR) approach. Despite its growing popularity and applications, however, no R package yet exists that makes this method available. In this paper, I introduce lpirfs, a fast and flexible R package that provides a broad framework to compute and visualize impulse response functions using LPs for a variety of data sets.

Resampling-Based Analysis Of Multivariate Data And Repeated Measures Designs With The R Package Manova.Rm, Sarah Friedrich, Frank Konietschke, Markus Pauly

The R Journal

Nonparametric statistical inference methods for a modern and robust analysis of longitudinal and multivariate data in factorial experiments are essential for research. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or equal covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p value and confidence interval estimates. Therefore, newly developed nonparametric statistical methods based on bootstrap- and permutation-approaches, which neither assume multivariate normality nor specific covariance matrices, have been …

The Landscape Of R Packages For Automated Exploratory Data Analysis, Mateusz Staniak, Przemysław Biecek

The R Journal

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering

There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). …

Roahd Package: Robust Analysis Of High Dimensional Data, Francesca Ieva, Anna Maria Paganoni, Juan Romo, Nicholas Tarabelloni

The R Journal

The focus of this paper is on the open-source R package roahd (RObust Analysis of High dimensional Data), see Tarabelloni et al. (2017). roahd has been developed to gather recently proposed statistical methods that deal with the robust inferential analysis of univariate and multivariate functional data. In particular, efficient methods for outlier detection and related graphical tools, methods to represent and simulate functional data, as well as inferential tools for testing differences and dependency among families of curves will be discussed, and the associated functions of the package will be described in details.

Jomo: A Flexible Package For Two-Level Joint Modelling Multiple Imputation, Matteo Quartagno, Simon Grund, James Carpenter

The R Journal

Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure — repeated observations on patients, school children nested in classes within schools within educational districts — the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model fitted by Markov Chain Monte Carlo (MCMC). Compared to previous packages for multilevel imputation, e.g. pan, jomo adds the …

Cvcrand: A Package For Covariate-Constrained Randomization And The Clustered Permutation Test For Cluster Randomized Trials, Hengshi Yu, Fan Li, John A. Gallis, Elizabeth L. Turner

The R Journal

The cluster randomized trial (CRT) is a randomized controlled trial in which randomization is conducted at the cluster level (e.g., school or hospital) and outcomes are measured for each individual within a cluster. Often, the number of clusters available to randomize is small (≤ 20), which increases the chance of baseline covariate imbalance between comparison arms. Such imbalance is particularly problematic when the covariates are predictive of the outcome because it can threaten the internal validity of the CRT. Pair-matching and stratification are two restricted randomization approaches that are frequently used to ensure balance at the design stage. An alternative, …

Biclustermd: An R Package For Biclustering With Missing Values, John Reisner, Hieu Pham, Sigurdur Olafsson, Stephen Vardeman, Jing Li

The R Journal

Biclustering is a statistical learning technique that attempts to find homogeneous partitions of rows and columns of a data matrix. For example, movie ratings might be biclustered to group both raters and movies. biclust is a current R package allowing users to implement a variety of biclustering algorithms. However, its algorithms do not allow the data matrix to have missing values. We provide a new R package, biclustermd, which allows users to perform biclustering on numeric data even in the presence of missing values.

Modeling Regimes With Extremes: The Bayesdfa Package For Identifying And Forecasting Common Trends And Anomalies In Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, Michael A. Litzow

The R Journal

The bayesdfa package provides a flexible Bayesian modeling framework for applying dynamic factor analysis (DFA) to multivariate time-series data as a dimension reduction tool. The core estimation is done with the Stan probabilistic programming language. In addition to being one of the few Bayesian implementations of DFA, novel features of this model include (1) optionally modeling latent process deviations as drawn from a Student-t distribution to better model extremes, and (2) optionally including autoregressive and moving-average components in the latent trends. Besides estimation, we provide a series of plotting functions to visualize trends, loadings, and model predicted values. A secondary …

Ppci: An R Package For Cluster Identification Using Projection Pursuit, David P. Hofmeyr, Nicos G. Pavlidis

The R Journal

This paper presents the R package PPCI which implements three recently proposed projection pursuit methods for clustering. The methods are unified by the approach of defining an optimal hyperplane to separate clusters, and deriving a projection index whose optimiser is the vector normal to this separating hyperplane. Divisive hierarchical clustering algorithms that can detect clusters defined in different subspaces are readily obtained by recursively bi-partitioning the data through such hyperplanes. Projecting onto the vector normal to the optimal hyperplane enables visualisations of the data that can be used to validate the partition at each level of the cluster hierarchy. Clustering …

Coxed: An R Package For Computing Duration-Based Quantities From The Cox Proportional Hazards Model, Jonathan Kropko, Jeffrey J. Harden

The R Journal

The Cox proportional hazards model is one of the most frequently used estimators in duration (survival) analysis. Because it is estimated using only the observed durations’ rank ordering, typical quantities of interest used to communicate results of the Cox model come from the hazard function (e.g., hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague and difficult for many audiences of research to understand. We introduce a suite of methods in the R package coxed to address these problems. The package allows researchers to calculate duration-based quantities from Cox model results, such as the expected …

Orthodr: Semiparametric Dimension Reduction Via Orthogonality Constrained, Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, Xin Zhang

The R Journal

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by Wen and Yin (2012) for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform …

Using Web Services To Work With Geodata In R, Jan-Philipp Kolb

The R Journal

Through collaborative mapping, a massive amount of data is accessible. Many individuals contribute information each day. The growing amount of geodata is gathered by volunteers or obtained via crowd-sourcing. One outstanding example of this is the OpenStreetMap (OSM) Project which provides access to big data in geography. Another online mapping service that enables the integration of geodata into the analysis is Google Maps. The expanding content and the availability of geographic information radically changes the perspective on geodata (Chilton 2009). Recently many application programming interfaces (APIs) have been built on OSM and Google Maps. That leads to a point where …

Spgarch: An R-Package For Spatial And Spatiotemporal Arch And Garch Models, Philipp Otto

The R Journal

In this paper, a general overview on spatial and spatiotemporal ARCH models is provided. In particular, we distinguish between three different spatial ARCH-type models. In addition to the original definition of Otto et al. (2016), we introduce an logarithmic spatial ARCH model in this paper. For this new model, maximum-likelihood estimators for the parameters are proposed. In addition, we consider a new complex-valued definition of the spatial ARCH process. Moreover, spatial GARCH models are briefly discussed. From a practical point of view, the use of the R-package spGARCH is demonstrated. To be precise, we show how the proposed spatial ARCH …

Hcmodelsets: An R Package For Specifying Sets Of Well-Fitting Models In High Dimensions, Henrique Hoeltgebaum, Heather Battey

The R Journal

In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of these explanations as is feasible. The standard practice, by contrast, is to report a single effective model for prediction. This paper illustrates the R implementation of the new ideas in the package HCmodelSets, using simple reproducible examples and real data. Results of some simulation experiments are also reported.

The R Package Trafo For Transforming Linear Regression Models, Lily Medina, Ann-Kristin Kreutzmann, Natalia Rojas-Perilla, Piedad Castro

The R Journal

Researchers and data-analysts often use the linear regression model for descriptive, predictive, and inferential purposes. This model relies on a set of assumptions that, when not satisfied, yields biased results and noisy estimates. A common problem that can be solved in many ways – use of less restrictive methods (e.g. generalized linear regression models or non-parametric methods ), variance corrections or transformations of the response variable just to name a few. We focus on the latter option as it allows to keep using the simple and well-known linear regression model. The list of transformations proposed in the literature is long …

Comparing Namedcapture With Other R Packages For Regular Expressions, Toby Dylan Hocking

The R Journal

Regular expressions are powerful tools for manipulating non-tabular textual data. For many tasks (visualization, machine learning, etc), tables of numbers must be extracted from such data before processing by other R functions. We present the R package namedCapture, which facilitates such tasks by providing a new user-friendly syntax for defining regular expressions in R code. We begin by describing the history of regular expressions and their usage in R. We then describe the new features of the namedCapture package, and provide detailed comparisons with related R packages (rex, stringr, stringi, tidyr, rematch2, re2r).

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.10 was released on 30 October, 2019. It is compatible with R 3.6.1 and consists of 1823 software packages, 384 experiment data packages, 953 up-to-date annotation packages, and 27 workflows. The release announcement includes descriptions of 94 new software packages, and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands

Fitting Tails By The Empirical Residual Coefficient Of Variation: The Ercv Package, Joan Del Castillo, Isabel Serra, Maria Padilla, David Moriña

The R Journal

This article is a self-contained introduction to the R package ercv and to the methodology on which it is based through the analysis of nine examples. The methodology is simple and trustworthy for the analysis of extreme values and relates the two main existing methodologies. The package contains R functions for visualizing, fitting and validating the distribution of tails. It also provides multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm.

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 4 months, 632 new packages were added to the CRAN package repository. 27 packages were unarchived and 182 were archived. The following shows the growth of the number of active packages in the CRAN package repository: