24,846 research outputs found
Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability
Analytical Potential Energy Function for the Ground State X^{1} Sigma^+ of LaCl
The equilibrium geometry, harmonic frequency and dissociation energy of
lanthanum monochloride have been calculated at B3LYP, MP2, QCISD(T) levels with
energy-consistent relativistic effective core potentials. The possible
electronic state and reasonable dissociation limit for the ground state are
determined based on atomic and molecular reaction statics. Potential energy
curve scans for the ground state X^{1} Sigma^+ have been carried out with B3LYP
and QCISD(T) methods due to their better performance in bond energy
calculations. We find the potential energy calculated with QCISD(T) method is
about 0.5 eV larger than dissociation energy when the diatomic distance is as
large as 0.8 nm. The problem that single-reference ab initio methods don't meet
dissociation limit during calculations of lanthanide heavy-metal elements is
analyzed. We propose the calculation scheme to derive analytical Murrell-Sorbie
potential energy function and Dunham expansion at equilibrium position.
Spectroscopic constants got by standard Dunham treatment are in good agreement
with results of rotational analyses on spectroscopic experiments. The
analytical function is of much realistic importance since it is possible to be
applied to predict fine transitional structure and study reaction dynamic
process.Comment: 10 pages, 1 figure, 3 table
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue
system, which can be divided into two phases: (1) sentence planning: deciding
the overall sentence structure, (2) surface realization: determining specific
word forms and flattening the sentence structure into a string. With the rise
of deep learning, most modern NLG models are based on a sequence-to-sequence
(seq2seq) model, which basically contains an encoder-decoder structure; these
NLG models generate sentences from scratch by jointly optimizing sentence
planning and surface realization. However, such simple encoder-decoder
architecture usually fail to generate complex and long sentences, because the
decoder has difficulty learning all grammar and diction knowledge well. This
paper introduces an NLG model with a hierarchical attentional decoder, where
the hierarchy focuses on leveraging linguistic knowledge in a specific order.
The experiments show that the proposed method significantly outperforms the
traditional seq2seq model with a smaller model size, and the design of the
hierarchical attentional decoder can be applied to various NLG systems.
Furthermore, different generation strategies based on linguistic patterns are
investigated and analyzed in order to guide future NLG research work.Comment: accepted by the 7th IEEE Workshop on Spoken Language Technology (SLT
2018). arXiv admin note: text overlap with arXiv:1808.0274
A New Outer Bound and the Noisy-Interference Sum-Rate Capacity for Gaussian Interference Channels
A new outer bound on the capacity region of Gaussian interference channels is
developed. The bound combines and improves existing genie-aided methods and is
shown to give the sum-rate capacity for noisy interference as defined in this
paper. Specifically, it is shown that if the channel coefficients and power
constraints satisfy a simple condition then single-user detection at each
receiver is sum-rate optimal, i.e., treating the interference as noise incurs
no loss in performance. This is the first concrete (finite signal-to-noise
ratio) capacity result for the Gaussian interference channel with weak to
moderate interference. Furthermore, for certain mixed (weak and strong)
interference scenarios, the new outer bounds give a corner point of the
capacity region.Comment: 20 pages, 8 figures, submitted to IEEE Trans. Inform. Theory
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