400 research outputs found
PourIt!: Weakly-supervised Liquid Perception from a Single Image for Visual Closed-Loop Robotic Pouring
Liquid perception is critical for robotic pouring tasks. It usually requires
the robust visual detection of flowing liquid. However, while recent works have
shown promising results in liquid perception, they typically require labeled
data for model training, a process that is both time-consuming and reliant on
human labor. To this end, this paper proposes a simple yet effective framework
PourIt!, to serve as a tool for robotic pouring tasks. We design a simple data
collection pipeline that only needs image-level labels to reduce the reliance
on tedious pixel-wise annotations. Then, a binary classification model is
trained to generate Class Activation Map (CAM) that focuses on the visual
difference between these two kinds of collected data, i.e., the existence of
liquid drop or not. We also devise a feature contrast strategy to improve the
quality of the CAM, thus entirely and tightly covering the actual liquid
regions. Then, the container pose is further utilized to facilitate the 3D
point cloud recovery of the detected liquid region. Finally, the
liquid-to-container distance is calculated for visual closed-loop control of
the physical robot. To validate the effectiveness of our proposed method, we
also contribute a novel dataset for our task and name it PourIt! dataset.
Extensive results on this dataset and physical Franka robot have shown the
utility and effectiveness of our method in the robotic pouring tasks. Our
dataset, code and pre-trained models will be available on the project page.Comment: ICCV202
Synthesis of vanadium oxide nanostructures for functional applications
Vanadium oxide nanoparticles have displayed excellent properties in the field of clean energy, environment, and catalysis. Nowadays, the applications of vanadium oxides in catalysts, lithium ion batteries (LIB), gas sensors, smart windows, and temperature switches attract increasing more attention. Therefore, the studies of the synthesis and properties of these materials are important for scaling up production and understanding the formation and growth mechanism, and surface behaviours for functional properties and potential applications.
In this thesis, a brief introduction of the relative research and a literature review on the vanadium oxides and their nanocomposites were presented in Chapters 1 and 2, respectively. Chapter 3 systematically described the preparation of various vanadium oxides (V2O5 and V2O3) with different shapes (microspheres, microurchins and nanorods) by a polythermal method, the growth mechanism, and the sensing performance of V2O5 nanoparticles. To enhance the function properties of gas sensing, the nanocomposites of silver vanadium oxides (SVO) and vanadium oxides with Ag nanocomposites (VOx@Ag) were investigated in Chapter 4, in which Ag2V4O11 nanobelts were found to exhibit high sensitivity and selectivity to amines. To enrich the application of V2O3 particles, Chapters 5 and 6 respectively demonstrated wet-chemical methods for induced synthesis of Ag nanowires and Ag-Au bimetallic nanowires by V2O3 particles. In these chapters, the formation mechanisms and catalytic performance for reduction of 4-nitrophenol were discussed. Finally, the conclusions were summarised in Chapter 7
Implementations of electric vehicle system based on solar energy in Singapore assessment of lithium ion batteries for automobiles
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 142-150).In this thesis report, both quantitative and qualitative approaches are used to provide a comprehensive analysis of lithium ion (Li-ion) batteries for plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) from technological and economical perspectives. Five key factors including power density, energy density, safety, durability, and cost are employed to compare four types of Li-ion batteries. Utility analysis indicates that all the Li-ion batteries are able to satisfy both power density and energy density targets, but only two of them are able to meet safety and durability requirements. Currently, the main challenge for their automotive application is cost reduction, since the cheapest LiFePOâ‚„ battery costs 4,270 and $7,726 of U.S. government subsidizations to an individual user are needed for PHEV and BEV to breakeven. Lastly, the lithium ion battery based electric vehicle systems have also been evaluated in the implementation models in Singapore. The conclusion is that it is not feasible to adopt electric vehicle system in Singapore under current government incentives.by Haitao Fu.M.Eng
Truncated Laplace and Gaussian mechanisms of RDP
The Laplace mechanism and the Gaussian mechanism are primary mechanisms in
differential privacy, widely applicable to many scenarios involving numerical
data. However, due to the infinite-range random variables they generate, the
Laplace and Gaussian mechanisms may return values that are semantically
impossible, such as negative numbers. To address this issue, we have designed
the truncated Laplace mechanism and Gaussian mechanism. For a given truncation
interval [a, b], the truncated Gaussian mechanism ensures the same Renyi
Differential Privacy (RDP) as the untruncated mechanism, regardless of the
values chosen for the truncation interval [a, b]. Similarly, the truncated
Laplace mechanism, for specified interval [a, b], maintains the same RDP as the
untruncated mechanism. We provide the RDP expressions for each of them. We
believe that our study can further enhance the utility of differential privacy
in specific applications
2,2′-(p-PhenylÂenedimethylÂene)bisÂ(propane-1,3-diol)
The molÂecule of the title compound, C14H22O4, is centrosymmetric. In the crystal, the molÂecules are linked through O—H⋯O hydrogen bonds into a three-dimensional network
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