1,721,469 research outputs found
Coupled mass-spring-damper system for nonlinear system identification - actuated with random static inputs - synthetically generated
No full textOverview
This dataset contains input-output data of a coupled mass-spring-damper system with a nonlinear force profile. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system coupled-msd. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution) can be found in the respective folders. After creating the dataset, the files are stored in the raw folder. Then, they are split into subsets for training, testing, and validation and can be found in the processed folder; details about the splitting are found in the config.json file.
The dataset can be used to test system identification algorithms and methods that aim to identify nonlinear dynamics from input-output measurements. The training dataset is used to optimize the model parameters, the validation set for hyperparameter optimization, and the test set only for the final evaluation.
In [2], the authors use the same underlying dynamics to create their dataset.
Input generation
Input trajectories are piecewise constant trajectories.
Noise
Gaussian white noise of approximately 30dB is added at the output.
Statistics
The input and output size is one.
In-distribution data: 1,500,000 data points
Training: 120 trajectories of length 7500
Validation: 20 trajectories of length 7500
Test: 60 trajectories of length 7500
Out-of-distribution data: 10 times 3000 data points
10 different datasets were only used for testing. Each dataset contains 50 trajectories of length 6000.
References
Frank, D. statesim [Computer software]. https://github.com/Dany-L/statesim
Revay, M., Wang, R., & Manchester, I. R. (2020). A convex parameterization of robust recurrent neural networks. IEEE Control Systems Letters, 5(4), 1363-1368.
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Damped pendulum for nonlinear system identification - inputs are sampled from a multivariate-normal distribution - synthetically generated
No full textOverview
This dataset contains input-output data of a damped nonlinear pendulum that is actuated at the mounting point. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system actuated pendulum. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution) can be found in the respective folders. After creating the dataset, the files are stored in the raw folder. Then, they are split into subsets for training, testing, and validation and can be found in the processed folder; details about the splitting are found in the config.json file.
The dataset can be used to test system identification algorithms and methods that aim to identify nonlinear dynamics from input-output measurements. The training dataset is used to optimize the model parameters, the validation set for hyperparameter optimization, and the test set only for the final evaluation.
In [2], the authors used the same underlying dynamics to create their dataset but without damping terms.
Input generation
Input trajectories are sampled from a multivariate-normal distribution.
Noise
Gaussian white noise of approximately 30dB is added at the output.
Statistics
The input and output size is one.
In-distribution data: 2 100 000 data points
Training: 10 000 trajectories of length 150
Validation: 2 000 trajectories of length 150
Test: 2 000 trajectories of length 150
Out-of-distribution data: 7 times 100 000 data points
7 different datasets were only used for testing. Each dataset contains 200 trajectories of length 500.
References
Frank, D. statesim [Computer software]. https://github.com/Dany-L/statesim
Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence, 3(3), 218-229.
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Code for statesim - a python package to simulate dynamical systems from ordinary differential equations.
No full textPython package for simulating ordinary differential equations in state space form. See README.md for details.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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