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 LiFePO₄ battery costs $247.8/kWh which is 1.65 times the cost target established by USABC. Economical values of PHEV and BEV are presented from an end user's point of view. Various sensitivity analysis have been used to identify the impact of key factors such as battery pack cost reduction, driving distance, gasoline price, and government subsidizations on cost effectiveness of PHEV and BEV. Results show that$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-Phenyl­enedimethyl­ene)bis­(propane-1,3-diol)

The mol­ecule of the title compound, C14H22O4, is centrosymmetric. In the crystal, the mol­ecules are linked through O—H⋯O hydrogen bonds into a three-dimensional network