Tong, Yan | Search NASA

DOE OSTI2017

MOF-derived multifractal porous carbon with ultrahigh lithium-ion storage performance

Porous carbon is one of the most promising alternatives to traditional graphite materials in lithium-ion batteries. This is not only attributed to its advantages of good safety, stability and electrical conductivity, which are held by all the carbon-based electrodes, but also especially ascribed to its relatively high capacity and excellent cycle stability. Here we report the design and synthesis of a highly porous pure carbon material with multifractal structures. This material is prepared by the vacuum carbonization of a zinc-based metal-organic framework, which demonstrates an ultrahigh lithium storage capacity of 2458 mAh g -1 and a favorable high-rate performance. The associations between the structural features and the lithium storage mechanism are also revealed by small-angle X-ray scattering (SAXS), especially the closed pore effects on lithium-ion storage.

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DOE OSTI2016

Visual-Attention-Based Background Modeling for Detecting Infrequently Moving Objects

Motion is one of the most important cues to separate foreground objects from the background in a video. Using a stationary camera, it is usually assumed that the background is static, while the foreground objects are moving most of the time. However, in practice, the foreground objects may show infrequent motions, such as abandoned objects and sleeping persons. Meanwhile, the background may contain frequent local motions, such as waving trees and/or grass. Such complexities may prevent the existing background subtraction algorithms from correctly identifying the foreground objects. We propose a new approach that can detect the foreground objects with frequent and/or infrequent motions. Specifically, we use a visual-attention mechanism to infer a complete background from a subset of frames and then propagate it to the other frames for accurate background subtraction. Furthermore, we develop a feature-matching-based local motion stabilization algorithm to identify frequent local motions in the background for reducing false positives in the detected foreground. The proposed approach is fully unsupervised, without using any supervised learning for object detection and tracking. Extensive experiments on a large number of videos have demonstrated that the proposed approach outperforms the state-of-the-art motion detection and background subtraction methods in comparison.

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