An Unsupervised Deep Learning Approach for Scenario Forecasts

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available at https://github.com/chennnnnyize/Scenario-Forecasts-GA

Domain Adaptation via Bidirectional Cross-Attention Transformer

Domain Adaptation (DA) aims to leverage the knowledge learned from a source domain with ample labeled data to a target domain with unlabeled data only. Most existing studies on DA contribute to learning domain-invariant feature representations for both domains by minimizing the domain gap based on convolution-based neural networks. Recently, vision transformers significantly improved performance in multiple vision tasks. Built on vision transformers, in this paper we propose a Bidirectional Cross-Attention Transformer (BCAT) for DA with the aim to improve the performance. In the proposed BCAT, the attention mechanism can extract implicit source and target mixup feature representations to narrow the domain discrepancy. Specifically, in BCAT, we design a weight-sharing quadruple-branch transformer with a bidirectional cross-attention mechanism to learn domain-invariant feature representations. Extensive experiments demonstrate that the proposed BCAT model achieves superior performance on four benchmark datasets over existing state-of-the-art DA methods that are based on convolutions or transformers

Physical properties of noncentrosymmetric superconductor Ru7_7B3_3

Transition metal boride Ru$_7$B$_3$ was found to be a noncentrosymmetric superconductor with $T_{C}$ equal to 3.3 K. Superconducting and normal state properties of Ru$_7$B$_3$ were determined by a self-consistent analysis through resistivity($\rho_{xx}$ and $\rho_{xy}$), specific heat, lower critical field measurement and electronic band structure calculation. It is found that Ru$_7$B$_3$ belongs to an s-wave dominated single band superconductor with energy gap 0.5 meV and could be categorized into type II superconductor with weak electron-phonon coupling. Unusual 'kink' feature is clearly observed in field-broadening resistivity curves, suggesting the possible mixture of spin triplet induced by the lattice without inversion symmetry.Comment: 11 pages, 16 figures. submitted to Phys. Rev.

Three-dimensional analysis of upper airway morphology in skeletal Class III patients with and without mandibular asymmetry

Objective: To compare the three-dimensional (3D) morphology of the upper airway in skeletal Class III patients with and without mandibular asymmetry and to investigate the possible underlying correlations between the morphology of the upper airway and mandibular deviation. Materials and Methods: Cone-beam computed tomography images of 54 subjects with skeletal Class III malocclusion (ANB angle <= 0.4 degrees, Wits <= -5.5 degrees) were taken and 3D upper airway models were reconstructed using Dolphin 3D software. According to the distance (d) from symphysis menti to the sagittal plane, all subjects were divided into a symmetry group (d <= 2 mm) and an asymmetry group (d >= 4 mm). Based on the severity of mandibular deviation, the asymmetry group was divided into subgroup I (4 mm <= d <10 mm) and subgroup II (d >= 10 mm). Cross-sectional linear distances, areas, and volumetric variables of the upper airway were measured in the 3D airway model. Results: Width of the inferior limit of the glossopharynx (P3W), cross-sectional area of the anterior limit of the nasal airway (P5S), and height of the glossopharynx (GPH) in the asymmetry group were significantly larger than in the symmetry group. As for subjects with severe mandibular deviation in subgroup II (d >= 10 mm), volume of the glossopharynx (GPV), total volume of the pharynx (TPV), length of the inferior limit of the velopharynx (P2L), and ratio of length to width of the inferior limit of the velopharynx (P2L/P2W) showed significantly negative correlations with mandibular deviation (r > 0.7, P <.05). Conclusions: In Class III subjects with severe mandibular asymmetry, the pharyngeal airway showed a tendency toward constriction and presented a more elliptical shape as mandibular deviation became more severe (P <.01).SCI(E)ARTICLE4526-5338