Abstract-The use of market mechanisms to determine generation dispatch, and the natural tendency to seek improved economic efficiency through rapid market updates, raises a critical issue. As the frequency of market-based dispatch updates increases, there will inevitably be interaction between the dynamics of markets determining the generator dispatch commands, and the physical response of generators and network interconnections. This paper examines questions of stability in such coupled systems by means of numeric tests using various market update models, (including detailed generator/turbine/governor dynamics) for the New England 39 bus test system. The results highlight the nature of potential instabilities and show the interaction modes between physical and market quantities through eigen-analysis. Understanding of potential modes of instability in such coupled systems is crucial both for designing suitable rules for power markets, and for designing physical generator controls that are compatible with market-based dispatch.
Abstract-Recently, it has been shown that the max flow capacity can be achieved in a multicast network using network coding. In this paper, we propose and analyze a more realistic model for wireless random networks. We prove that the capacity of network coding for this model is concentrated around the expected value of its minimum cut. Furthermore, we establish upper and lower bounds for wireless nodes using Chernoff bounds. Our experiments show that our theoretical predictions are well matched by simulation results.
Recurrent neural networks (RNNs) have been applied to a broad range of applications including natural language processing, drug discovery, and video recognition. However, their vulnerability to input perturbation is also exposed. Aligning with a view from software defect detection, this paper aims to develop a coverage guided testing approach to systematically exploit the internal behaviour of RNNs, with high possibility of detecting defects. Technically, the long short term memory network (LSTM), a major class of RNN, is thoroughly studied. A family of three test metrics are designed to quantify not only the values but also the temporal relations (including both step-wise and bounded-length) exhibited when LSTM processing inputs. A genetic algorithm is applied to efficiently generate test cases. Based on these, we develop a tool TESTRNN, and extensively evaluate TESTRNN on a set of LSTM benchmarks. Experiments confirm that TESTRNN has several advantages over the state-of-art tool DeepStellar and attack-based defect detection methods, owing to its working with finer temporal semantics and the consideration of the naturalness of input perturbation. Furthermore, TESTRNN enables meaningful information to be collected and exhibited for users to understand the testing results, which is an important step towards interpretable neural network testing.
We calculate the single-particle resonances in a one-dimensional model potential and isotropic three-dimensional harmonic and Woods-Saxon potentials using the real stabilization method in coordinate space. The results of the real stabilization method are in good agreement with those from the scattering phase shift method and the analytical continuation in the coupling constant method.
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