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The Holst Centre was envisioning and anticipating new application areas where their technology platform could show its advantage.
Here we compare this method to the nonlinear Sigma-Point approach for a nonlinear, multi-stable model and show its advantage for model discrimination, especially for large parameter variances.
We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.
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Then, each method is analyzed to show its advantages and disadvantages.
In the followings, our approach is compared with the related work approaches to show its advantages.
To validate the method and show its advantages, an initial construction schedule example is created.
For this purpose we employ the new composite structure diagram and show its advantages over already established approaches.
Simulations using a model of RTS charging concept show its advantages compared to existing "valley-filling" techniques from the literature.
The evaluation of the system with three different groups of test persons is presented to show its advantages.
The robust optimization model composed of mean and SD is constructed and solved, the result of which is compared with the deterministic one to show its advantages.
The successful experimental results verify the feasibility of the proposed method, and show its advantages on aspects of high efficiency and easy processing as well.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com