Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

Quantum information processing architecture with endohedral fullerenes in a carbon nanotube

A potential quantum information processor is proposed using a fullerene peapod, i.e., an array of the endohedral fullerenes 15N@C60 or 31P@C60 contained in a single walled carbon nanotube (SWCNT). The qubits are encoded in the nuclear spins of the doped atoms, while the electronic spins are used for initialization and readout, as well as for two-qubit operations.Comment: 8 pages, 8 figure

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.Comment: Accepted & forthcoming at ITNG-201

Simplified TeV leptophilic dark matter in light of DAMPE data

Using a simplified framework, we attempt to explain the recent DAMPE cosmic $e^+ + e^-$ flux excess by leptophilic Dirac fermion dark matter (LDM). The scalar ($\Phi_0$) and vector ($\Phi_1$) mediator fields connecting LDM and Standard Model particles are discussed. Under constraints of DM relic density, gamma-rays, cosmic-rays and Cosmic Microwave Background (CMB), we find that the couplings $P \otimes S$, $P \otimes P$, $V \otimes A$ and $V \otimes V$ can produce the right bump in $e^+ + e^-$ flux for a DM mass around 1.5 TeV with a natural thermal annihilation cross-section $\sim 3 \times 10^{-26} cm^3/s$ today. Among them, $V \otimes V$ coupling is tightly constrained by PandaX-II data (although LDM-nucleus scattering appears at one-loop level) and the surviving samples appear in the resonant region, $m_{\Phi_1} \simeq 2m_{\chi}$. We also study the related collider signatures, such as dilepton production $pp \to \Phi_1 \to \ell^+\ell^-$, and muon $g-2$ anomaly. Finally, we present a possible $U(1)_X$ realization for such leptophilic dark matter.Comment: discussions added, version accepted by JHE