93,683 research outputs found
Data-Driven Approach to Simulating Realistic Human Joint Constraints
Modeling realistic human joint limits is important for applications involving
physical human-robot interaction. However, setting appropriate human joint
limits is challenging because it is pose-dependent: the range of joint motion
varies depending on the positions of other bones. The paper introduces a new
technique to accurately simulate human joint limits in physics simulation. We
propose to learn an implicit equation to represent the boundary of valid human
joint configurations from real human data. The function in the implicit
equation is represented by a fully connected neural network whose gradients can
be efficiently computed via back-propagation. Using gradients, we can
efficiently enforce realistic human joint limits through constraint forces in a
physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see
https://youtu.be/wzkoE7wCbu
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
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
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