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