325 research outputs found

    The Study of Sustainable Energy at UB

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    Sustainable energy includes solar energy, wind energy, hydropower, geothermal energy, and biomass. It has environmental, health and cost advantages over fossil fuels. Therefore, it attracts more and more attention on the energy harvesting, conversion, and distributed storage. In this paper, the facility and current research topics in sustainable energy are presented. In the mean time, the energy-related courses are introduced for the training of the next generation of engineers

    Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective

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    This study proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities. As a result, CVM detects the mismatch between the prior dynamic model and actual motion pattern to correct the filtering gain, and simultaneously becomes a nonlinear and state-coupled model with multiplicative noise. The study considers CVM to design a feasible distributed weighted least squares (WLS) filter. The WLS criterion requires a linear state-space model containing only additive noise about the estimated state. To meet the requirement, we derive such two separate pseudo-linearized models by using the first-order Taylor series expansion. The separation is merely in form, and the estimates of interested states are embedded as parameters into each other's model, which implies that their interdependency is still preserved in the iterative operation of two linear filters. With the two models, we first propose a centralized WLS filter by converting the measurements from all nodes into a summation form. Then, a distributed consensus scheme, which directly performs an inner iteration on the priors across different nodes, is proposed to incorporate the cross-covariances between nodes. Under the consensus scheme, a distributed WLS filter over a realistic network with ``naive'' node is developed by proper weighting of the priors and measurements. Finally, the performance of proposed filters in terms of accuracy, robustness, and consistency is testified under different prior situations.Comment: Corrected Versio

    Design of Residential Hydrogen Fueling System in UB Bodine Hall

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    Pollution and emission from the burning fossil fuel already became a serious environmental issue. To solve this problem, more and more green energy or renewable energy has been used into and impact onto modern society. For instance, solar energy, hydrogen, and wind turbine gradually play an important role in manufactory industry. Using hydrogen fueling system for the residential is good for environments, because the students whose live in dormitory have their own cars. If those cars are hydrogen car, they can use the fueling system to supply the hydrogen to the cars. They do not have to go the hydrogen gas station to gas the hydrogen. And using hydrogen is more eco friendly than using the burning fossil. This design is to produce hydrogen for the hydrogen cars belonged to the residents of Bodine Hall, a dormitory at the UB, through a solar energy powered system. In this design, a Proton-Exchange-Membrane (PEM) eletrolyzer is used as a hydrogen generator, solar panels are used to convert solar energy to electricity for electrolyzer, and a hydrogen compressor system is used to compress hydrogen and store it

    Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

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    Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method may not only cause the "many-to-one" problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions. In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first retrieves the model-confident alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments. To achieve this, we further propose an enhanced alignment model that is built on the current state-of-the-art baseline. In addition, to address the many-to-one problem, we propose to jointly predict entity alignments so that the one-to-one constraint can be naturally incorporated into the alignment prediction. Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also significantly improve existing baselines.Comment: in AAAI 202

    Flat electronic band structure and anisotropic optical, mechanical, and thermoelectric properties of two-dimensional fullerene networks

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    Nanoclusters like fullerenes as the unit to build intriguing two-dimensional topological structures is of great challenge. Here we propose three bridged fullerene monolayers and comprehensively investigate the novel fullerene monolayer as synthesized experimentally Zheng et al.,[Nature 606, 507-510 (2022)] by state of the art first principles calculations. Our results show that alpha-C60-2D has a direct bandgap of 1.49 eV owing to a flat conduction band bottom close to the experimental value, the optical linear dichroism with strong absorption in long-wave ultraviolet region, a small anisotropic Youngs modulus, the large hole mobility, and the ultrahigh Seebeck coefficient at middle low temperatures. Moreover, Li ions are found to migrate easily along the X path in alpha-C60-2D. It is unveiled that the anisotropic optical, mechanical, electrical, and thermoelectric properties of alpha-C60-2D originate from the asymmetric bridging arrangements between C60 clusters. Our study promises potential applications of monolayer fullerene networks in diverse fields

    DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics

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    Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning algorithms to train a policy with analytic gradients through environment dynamics, which significantly facilitates the development of DOM algorithms. However, existing DOM benchmarks are either single-object-based or non-differentiable. This leaves the questions of 1) how a task-specific algorithm performs on other tasks and 2) how a differentiable-physics-based algorithm compares with the non-differentiable ones in general. In this work, we present DaXBench, a differentiable DOM benchmark with a wide object and task coverage. DaXBench includes 9 challenging high-fidelity simulated tasks, covering rope, cloth, and liquid manipulation with various difficulty levels. To better understand the performance of general algorithms on different DOM tasks, we conduct comprehensive experiments over representative DOM methods, ranging from planning to imitation learning and reinforcement learning. In addition, we provide careful empirical studies of existing decision-making algorithms based on differentiable physics, and discuss their limitations, as well as potential future directions.Comment: ICLR 2023 (Oral
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