Search results for "Statistic"
showing 10 items of 12520 documents
Fallzahlplanung in referenzkontrollierten Diagnosestudien
2002
Purpose: A tutorial illustration of a flexible approach to determine the sample size in reference-controlled diagnostic trials. Materials and Methods: Assuming the usual setting of a new diagnostic method to be compared with a reference method, the emphasis is on the sensitivity of the new method in comparison with the reference method, using a binary outcome (positive versus negative) for both methods. Based on the confidence interval of the sensitivity, a simple but flexible procedure for determining the sample size is described, which incorporates clinically interpretable information. The procedure is illustrated by the fictious planning of a trial to assess the diagnostic value of MRI v…
Feature extraction from remote sensing data using Kernel Orthonormalized PLS
2007
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
Object Classification Technique for mmWave FMCW Radars using Range-FFT Features
2021
In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77- 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logist…
Comprehensive Strategy for Proton Chemical Shift Prediction: Linear Prediction with Nonlinear Corrections
2014
A fast 3D/4D structure-sensitive procedure was developed and assessed for the chemical shift prediction of protons bonded to sp3carbons, which poses the maybe greatest challenge in the NMR spectral parameter prediction. The LPNC (Linear Prediction with Nonlinear Corrections) approach combines three well-established multivariate methods viz. the principal component regression (PCR), the random forest (RF) algorithm, and the k nearest neighbors (kNN) method. The role of RF is to find nonlinear corrections for the PCR predicted shifts, while kNN is used to take full advantage of similar chemical environments. Two basic molecular models were also compared and discussed: in the MC model the desc…
Analysis on channel bonding/aggregation for multi-channel cognitive radio networks
2010
Channel bonding/aggregation techniques, which assemble several channels together as one channel, could be used in cognitive radio networks for the purpose of achieving better bandwidth utilization. In existing work on this topic, channel bonding/aggregation is focused on the cases when primary channels are time slotted or stationary as compared with secondary users' activities. In this paper, we analyze the performance of channel bonding/aggregation strategies when primary channels are not time slotted and the time scale of primary activities is at the same level as the secondary users', given that spectrum handover is not allowed. Continuous time Markov chain models are built in order to a…
A comparative molecular dynamics-phase-field modeling approach to brittle fracture
2016
Abstract In this work, a novel comparative method for highly brittle materials such as aragonite crystals is proposed, which provides an efficient and accurate in-sight understanding for multi-scale fracture modeling. In particular, physically-motivated molecular dynamics (MD) simulations are performed to model quasi-static brittle crack propagation on the nano-scale and followingly compared to macroscopic modeling of fracture using the phase-field modeling (PFM) technique. A link between the two modeling schemes is later proposed by deriving PFM parameters from the MD atomistic simulations. Thus, in this combined approach, MD simulations provide a more realistic meaning and physical estima…
Prediction Model Selection and Spare Parts Ordering Policy for Efficient Support of Maintenance and Repair of Equipment
2010
The prediction model selection problem via variable subset selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. Several papers have dealt with various aspects of the problem but it appears that the typical regression user has not benefited appreciably. One reason for the lack of resolution of the problem is the fact that it has not been well defined. Indeed, it is apparent that there is not a single probl…
Precise and efficient parametric path analysis
2012
Hard real-time systems require tasks to finish in time. To guarantee the timeliness of such a system, static timing analyses derive upper bounds on the worst-case execution time (WCET) of tasks. There are two types of timing analyses: numeric and parametric. A numeric analysis derives a numeric timing bound and, to this end, assumes all information such as loop bounds to be given a priori. If these bounds are unknown during analysis time, a parametric analysis can compute a timing formula parametric in these variables. A performance bottleneck of timing analyses, numeric and especially parametric, is the so-called path analysis, which determines the path in the analyzed task with the longes…
Flexible design of multifocal metalenses based on autofocused Airy beams
2018
Extreme miniaturization of on-demand optical devices such as ultrathin lenses is currently leading to significant advancements in manufacturing novel materials and nanotechnologies. Flexibility and tunability of engineered layouts enable efficient integration of complex photonic modules. In this regard, here we propose an autofocused Airy (AFA)-based metalens that operates, depending on the molded phase profile, as a multifocal focusing lens, which to the best of our knowledge has not been reported before. To do this, we call attention to the fact that the two conjugate focal points of an AFA beam can be brought into real space by applying a proper convex lens phase profile. Considering ful…
Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes
2019
BACKGROUND: In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. METHODS: From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings rep…