Disentangling Noise and Fluctuations in Data Sets of Complex Systems

2005 ◽  
pp. 83-94
Author(s):  
R. Friedrich ◽  
D. Kleinhans ◽  
J. Peinke
Keyword(s):  
Complex Systems ◽  
Data Sets

Biopower imagined: Biotechnological art and life engineering

2017 ◽  
Vol 57 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Eva Šlesingerová
Keyword(s):  
Complex Systems ◽  
Contemporary Art ◽  
Genetic Data ◽  
The Body ◽  
Data Sets ◽  
Cultural Environment ◽  
Living Tissues ◽  
Derivatives Of

We are witnessing profound changes in our societies via biosciences, biotechnologization, and digitalization. The influence and application of specific engineering rationality and cybernetic perspectives to the complex systems of living structures and to the language of biology are integral parts of our cultural environment. The biotechnological reproduction of life, bodies and cells, as well as AI-equipped machines, has become normal and sometimes even technically routine in contemporary societies. The biotechnologization of society and the engineering of life have also significantly influenced contemporary art fields, practices and projects. The crucial analytical scope for this article is a specific biotechnological art field – bio art. Bio art includes the works of artists who are intrigued by working with living or semi-living tissues and biotechnologies. Using specific artworks, mainly by Louis Bec, Heather Dewey-Hagborg, and Biononymous, the text investigates current forms of power over life – biopower – that imagine, classify, and govern our societies today, even on molecular and genetic levels. The text analyzes artistic reflections of the processes by which people are governed mainly as the derivatives of the body, biological and genetic data sets. In this context, the article explores artworks inspired by specific biopolitical engineering rationality and surveillance practices enabling naming, fabricating and dealing with life which is synthesized, ethnicized and monitored.

scholarly journals EEG Correlates of Visual Signal Processing: Spectral Decomposition using ICA

2019 ◽  
Vol 8 (4) ◽  
pp. 582-586
Keyword(s):  
Signal Processing ◽  
Complex Systems ◽  
Biological Activities ◽  
Visual Signal ◽  
Data Sets ◽  
Ecg Signals ◽  
Functional Behavior ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Enormous Amount

Studies on signal processing of biological activities is a way to ascertain the anatomical and functional behavior of complex systems. Many biomedical signals represent their significance for understanding the systems, like ECG signals of heart and EEG, MEG signals of brain’s electro- and magnetobehavior reveals their importance. Availability of enormous amount of EEG data, with unwanted noise and artifacts, from complex systems, is challenging to uncover the underlying dynamics of signals sources. Functional analysis of this data requires various methods to remove the artifacts and noise present in it and further investigation of the system dynamics. In this paper, we discussed the removal of artifacts and noise from brain’s EEG activity to achieve artifact rejections of continuous EEG data and to apply Independent Component Analysis (ICA) to analyze the event-related tasks. Cluster decomposed signal components allow us to visualize the independent components of any number of subjects and subject groups. Differentiating vast electrical activity of an abnormal subject in comparison with a normal subject is possible with the decomposing signal. ICA approach helps in correlating the event-related EEG signals for a subject in two or more conditions of the same experiment; hence, it suggests in creating the data sets of epochs for every condition.

scholarly journals Complex systems analysis informs on the spread of COVID-19

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Xia Wang ◽  
Dorcas Washington ◽  
Georg F. Weber
Keyword(s):  
Complex Systems ◽  
Three Dimensions ◽  
Data Sets ◽  
Time Lags ◽  
Complex Data ◽  
Public Health Response ◽  
Box Counting ◽  
Systems Research ◽  
Box Counting Dimension ◽  
Time Lagged

Abstract Objectives The non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. Methods To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Results Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment vs. exacerbation. Homogeneity or heterogeneity in the population response, uptick vs. suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. Conclusions The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can extract important characteristics and aid decision making in the public health response.

scholarly journals Complex Systems Analysis Informs on the Spread of COVID-19

2021 ◽  
Author(s):  
Xia Wang ◽  
Dorcas Washington ◽  
Georg F. Weber
Keyword(s):  
Complex Systems ◽  
Three Dimensions ◽  
Data Sets ◽  
Time Lags ◽  
Complex Data ◽  
Public Health Response ◽  
Box Counting ◽  
Systems Research ◽  
Box Counting Dimension ◽  
Time Lagged

AbstractThe non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment versus exacerbation. Homogeneity or heterogeneity in the population response, uptick versus suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can aid decision making in the public health response.

scholarly journals Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6841
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres
Keyword(s):  
Complex Systems ◽  
Expert Knowledge ◽  
Health Management ◽  
Low Cost ◽  
Data Preprocessing ◽  
Data Driven ◽  
Data Sets ◽  
Health State ◽  
Prognostics And Health Management ◽  
Monitoring Networks

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers

2021 ◽  
Author(s):  
Ehsan Kharazmi ◽  
Zhicheng Wang ◽  
Dixia Fan ◽  
Samuel Rudy ◽  
Themis Sapsis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Complex Systems ◽  
Fatigue Damage ◽  
Complete Characterization ◽  
Sensor Data ◽  
Data Sets ◽  
Multiple Sources ◽  
Marine Risers ◽  
Vortex Induced Vibrations

Abstract Assessing the fatigue damage in marine risers due to vortex-induced vibrations (VIV) serves as a comprehensive example of using machine learning methods to derive assessment models of complex systems. A complete characterization of response of such complex systems is usually unavailable despite massive experimental data and computation results. These algorithms can use multi-fidelity data sets from multiple sources, including real-time sensor data from the field, systematic experimental data, and simulation data. Here we develop a three-pronged approach to demonstrate how tools in machine learning are employed to develop data-driven models that can be used for accurate and efficient fatigue damage predictions for marine risers subject to VIV.

scholarly journals Managing Complex Dynamical Systems

2011 ◽  
Vol 4 (3) ◽  
Author(s):  
John C. Cox ◽  
Robert L. Webster ◽  
Jeanie A. Curry ◽  
Kevin L. Hammond
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Data Sets ◽  
Complex Dynamical Systems ◽  
Managerial Action ◽  
Insight Into

Management commonly engages in a variety of research designed to provide insight into the motivation and relationships of individuals, departments, organizations, etc. This paper demonstrates how the application of concepts associated with the analysis of complex systems applied to such data sets can yield enhanced insights for managerial action.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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