Tim Krake
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NMF-Based Analysis of Mobile Eye-Tracking Data Open
The depiction of scanpaths from mobile eye-tracking recordings by thumbnails\nfrom the stimulus allows the application of visual computing to detect areas of\ninterest in an unsupervised way. We suggest using nonnegative matrix\nfactorizat…
Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess Open
Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this article, we present an extension of STL to uncertain data, named uncertainty-aware STL (UASTL). Our method propagates multiv…
View article: Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frame Structures
Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frame Structures Open
Large statically indeterminate truss and frame structures exhibit complex load-bearing behavior, and redundancy matrices are helpful for their analysis and design. Depending on the task, the full redundancy matrix or only its diagonal entr…
View article: Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frame Structures
Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frame Structures Open
Large statically indeterminate truss and frame structures exhibit complex load-bearing behavior, and redundancy matrices are helpful for their analysis and design. Depending on the task, the full redundancy matrix or only its diagonal entr…
View article: Efficient Update of Redundancy Matrices for Truss and Frame Structures
Efficient Update of Redundancy Matrices for Truss and Frame Structures Open
Redundancy matrices provide insights into the load carrying behavior of statically indeterminate structures. This information can be employed for the design and analysis of structures with regard to certain objectives, for example reliabil…
View article: Review of “Efficient update of redundancy matrices for truss and frame structures”
Review of “Efficient update of redundancy matrices for truss and frame structures” Open
This is the Open Review of the article "Efficient update of redundancy matrices for truss and frame structures” by Krake et al. published in JTCAM (doi 10.46298/jtcam.9615)
Reduced Connectivity for Local Bilinear Jacobi Sets Open
We present a new topological connection method for the local bilinear computation of Jacobi sets that improves the visual representation while preserving the topological structure and geometric configuration. To this end, the topological s…
View article: Efficient Update of Redundancy Matrices for Truss and Frame Structures
Efficient Update of Redundancy Matrices for Truss and Frame Structures Open
Redundancy matrices provide insights into the load carrying behavior of statically indeterminate structures. This information can be employed for the design and analysis of structures with regard to certain objectives, for example reliabil…
Efficient and Robust Background Modeling with Dynamic Mode Decomposition Open
A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the mi…
Uncertainty-Aware Multidimensional Scaling Open
We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dime…
Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution Open
We introduce relaxed dot plots as an improvement of nonlinear dot plots for unit visualization. Our plots produce more faithful data representations and reduce moiré effects. Their contour is based on a customized kernel frequency estimati…
Constrained Dynamic Mode Decomposition Open
Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processi…
Visualization and selection of Dynamic Mode Decomposition components for unsteady flow Open
Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a low-dimensio…
Unsupervised and Generic Short-Term Anticipation of Human Body Motions Open
Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use…
Dynamic Mode Decomposition: Theory and Data Reconstruction Open
Dynamic Mode Decomposition (DMD) is a data-driven decomposition technique extracting spatio-temporal patterns of time-dependent phenomena. In this paper, we perform a comprehensive theoretical analysis of various variants of DMD. We provid…