103 research outputs found
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
At the heart of power system operations, alternating current optimal power
flow (ACOPF) studies the generation of electric power in the most economical
way under network-wide load requirement, and can be formulated as a highly
structured non-convex quadratically constrained quadratic program (QCQP).
Optimization-based solutions to ACOPF (such as ADMM or interior-point method),
as the classic approach, require large amount of computation and cannot meet
the need to repeatedly solve the problem as load requirement frequently
changes. On the other hand, learning-based methods that directly predict the
ACOPF solution given the load input incur little computational cost but often
generates infeasible solutions (i.e. violate the constraints of ACOPF). In this
work, we combine the best of both worlds -- we propose an innovated framework
for learning ACOPF, where the input load is mapped to the ACOPF solution
through a neural network in a computationally efficient and reliable manner.
Key to our innovation is a specific-purpose "activation function" defined
implicitly by a QCQP and a novel loss, which enforce constraint satisfaction.
We show through numerical simulations that our proposed method achieves
superior feasibility rate and generation cost in situations where the existing
learning-based approaches fail
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
CycleGAN is capable of learning a one-to-one mapping between two data
distributions without paired examples, achieving the task of unsupervised data
translation. However, there is no theoretical guarantee on the property of the
learned one-to-one mapping in CycleGAN. In this paper, we experimentally find
that, under some circumstances, the one-to-one mapping learned by CycleGAN is
just a random one within the large feasible solution space. Based on this
observation, we explore to add extra constraints such that the one-to-one
mapping is controllable and satisfies more properties related to specific
tasks. We propose to solve an optimal transport mapping restrained by a
task-specific cost function that reflects the desired properties, and use the
barycenters of optimal transport mapping to serve as references for CycleGAN.
Our experiments indicate that the proposed algorithm is capable of learning a
one-to-one mapping with the desired properties.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI
2019
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
The rapid development and wide utilization of object detection techniques
have aroused attention on both accuracy and speed of object detectors. However,
the current state-of-the-art object detection works are either
accuracy-oriented using a large model but leading to high latency or
speed-oriented using a lightweight model but sacrificing accuracy. In this
work, we propose YOLObile framework, a real-time object detection on mobile
devices via compression-compilation co-design. A novel block-punched pruning
scheme is proposed for any kernel size. To improve computational efficiency on
mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced
compiler-assisted optimizations. Experimental results indicate that our pruning
scheme achieves 14 compression rate of YOLOv4 with 49.0 mAP. Under our
YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung
Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the
inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4
by 5 speedup. Source code is at:
\url{https://github.com/nightsnack/YOLObile}
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