Temporal logic inference is the process of extracting formal descriptions of system behaviors from data in the form of temporal logic formulas. The existing temporal logic inference methods mostly neglect uncertainties in the data, which results in limited applicability of such methods in real-world deployments. In this paper, we first investigate the uncertainties associated with trajectories of a system and represent such uncertainties in the form of interval trajectories. We then propose two uncertaintyaware signal temporal logic (STL) inference approaches to classify the undesired behaviors and desired behaviors of a system. Instead of classifying finitely many trajectories, we classify infinitely many trajectories within the interval trajectories. In the first approach, we incorporate robust semantics of STL formulas with respect to an interval trajectory to quantify the margin at which an STL formula is satisfied or violated by the interval trajectory. The second approach relies on the first learning algorithm and exploits the decision tree to infer STL formulas to classify behaviors of a given system. The proposed approaches also work for non-separable data by optimizing the worst-case robustness in inferring an STL formula. Finally, we evaluate the performance of the proposed algorithms in two case studies, where the proposed algorithms show reductions in the computation time by up to four orders of magnitude in comparison with the sampling-based baseline algorithms (for a dataset with 800 sampled trajectories in total).
Recently, multi-stable origami structures and material systems have shown promising potentials to achieve multi-functionality. Especially, origami folding is fundamentally a three-dimensional mechanism, which imparts unique capabilities not seen in the more traditional multi-stable systems. This paper proposes and analytically examines a multi-stable origami cellular structure that can exhibit asymmetric energy barriers and a mechanical diode behavior in compression. Such a structure consists of many stacked Miura-ori sheets of different folding stiffness and accordion-shaped connecting sheets, and it can be divided into unit cells that features two different stable equilibria. To understand the desired diode behavior, this study focuses on two adjacent unit cells and examines how folding can create a kinematic constraint onto the deformation of these two cells. Via estimating the elastic potential energy landscape of this dual cell system. we find that the folding-induced kinematic constraint can significantly increase the potential energy barrier for compressing the dual-cell structure from a certain stable state to another, however, the same constraint would not increase the energy barrier of the opposite extension switch. As a result, one needs to apply a large force to compress the origami cellular structure but only a small force to stretch it, hence a mechanical diode behavior. Results of this study can open new possibilities for achieving structural motion rectifying, wave propagation control, and embedded mechanical computation.
Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user's preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.
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