An official website of the United States government
Abstract Silicon anodes have been demonstrated to provide significant actuation in addition to energy storage in lithium-ion batteries (LIBs). This work studies the optimization of 1D unimorph and bimorph actuators to achieve a target shape upon actuation. A 1D shape matching with design optimization is used to estimate the varied charge distribution along the length for a LIB actuator and thereby the effect of distance between electrodes in charging. A genetic algorithm (GA) is used with actuation strain distribution as the design variable. The objective of the optimization is to shape-match by minimizing the shape error between a target shape and actuated shape, both defined by several points along the length. The approach is experimentally validated by shape matching a notched unimorph target shape. A shape error of 1.5% is obtained. An optimized unimorph converges to an objective function of less than 0.029% of the length at full state of charge (SOC) for a 5-segment beam. A second shape matching case study using a bimorph is investigated to showcase the tailorability of LIB actuators. The optimal bimorph achieves an objective function of less than 0.23% of the length for a design variable set of top and bottom actuation strain of an 8-segment beam. The actuated shape nearly matches the target shape by simultaneously activating top and bottom active layers to achieve the same differential actuation strain (the difference between top and bottom active layer actuation strain). The results show that a bimorph actuator can achieve a given shape while also storing significantly more charge than is necessary to maintain a given complex shape. This demonstrates a strength of energy storage based actuators: excess energy can be stored within the actuator and can be expended without affecting the work done or the shape maintained by the actuator.
more »
« less
Image Shape Classification with the Weighted Euler Curve Transform
The weighted Euler curve transform (WECT) was recently introduced as a tool to extract meaningful information from shape data, when the shape is equipped with a weight function. In this extended abstract, we provide an experimental investigation on using the WECT for image classification.
more »
« less
- Award ID(s):
- 1854336
- PAR ID:
- 10481317
- Publisher / Repository:
- CG Week Young Researcher's Forum
- Date Published:
- Subject(s) / Keyword(s):
- Euler characteristic transform, topology, shape, image classification
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We introduce the shape module of the Python package Geomstats to analyze shapes of objects represented as landmarks, curves and surfaces across fields of natural sciences and engineering. The shape module first implements widely used shape spaces, such as the Kendall shape space, as well as elastic spaces of discrete curves and surfaces. The shape module further implements the abstract mathematical structures of group actions, fiber bundles, quotient spaces and associated Riemannian metrics which allow users to build their own shape spaces. The Riemannian geometry tools enable users to compare, average, interpolate between shapes inside a given shape space. These essential operations can then be leveraged to perform statistics and machine learning on shape data. We present the object-oriented implementation of the shape module along with illustrative examples and show how it can be used to perform statistics and machine learning on shape spaces.more » « less
-
null (Ed.)We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data.more » « less
-
Soft robot deformations are typically estimated using strain sensors to infer change from a nominal shape while taking a robot‐specific mechanical model into account. This approach performs poorly during buckling and when material properties change with time, and is untenable for shape‐changing robots that don't have a well‐defined resting (unactuated) shape. Herein, these limitations are overcome using stretchable shape sensing (S3) sheets that fuse orientation measurements to estimate 3D surface contours without making assumptions about the underlying robot geometry or material properties. The S3 sheets can estimate the shape of target objects to an accuracy of ≈3 mm for an 80 mm long sheet. The authors show the S3 sheets estimating their shape while being deformed in 3D space and also attached to the surface of a silicone three‐chamber pneumatic bladder, highlighting the potential for shape‐sensing sheets to be applied, removed, and reapplied to soft robots for shape estimation. Finally, the S3 sheets detecting their own stretch up to 30% strain is demonstrated. The approach introduced herein provides a generalized method for measuring the shape of objects without making strong assumptions about the objects, thus achieving a modular, mechanics model‐free approach to proprioception for wearable electronics and soft robotics.more » « less
-
Chen, Tsu-Wei; Long, Stephen P (Ed.)Abstract Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of ‘hidden’ shape nuances which are otherwise not detected.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Workshop Report:
- The DOI is not currently available.
