Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction

We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points. Unlike the existing approaches, which mainly handle univariate time series for multi-step prediction or multivariate time series for single-step prediction, ETN-ODE could model multivariate time series for arbitrary-step prediction. In addition, it enjoys a tandem attention, w.r.t. temporal attention and variable attention, being able to provide explainable insights into the data. Specifically, ETN-ODE combines an explainable Tensorized Gated Recurrent Unit (Tensorized GRU or TGRU) with Ordinary Differential Equations (ODE). The derivative of the latent states is parameterized with a neural network. This continuous-time ODE network enables a multi-step prediction at arbitrary time points. We quantitatively and qualitatively demonstrate the effectiveness and the interpretability of ETN-ODE on five different multi-step prediction tasks and one arbitrary-step prediction task. Extensive experiments show that ETN-ODE can lead to accurate predictions at arbitrary time points while attaining best performance against the baseline methods in standard multi-step time series prediction

Effect of Height Difference on The Performance of Two-phase Thermosyphon Loop Used in Air-conditioning System

Two-phase thermosyphon loops (TPTLs) are highly effective devices for spontaneously transferring heat through a relatively long distance. Therefore, TPTLs are extensively used in various fields, such as cooling of electronic components, light water reactors, etc. Recently, the TPTL has also been found to be an effective way to recover or transfer heat in air-conditioning systems for energy saving. A typical TPTL consists of an evaporator, a riser (gas tube), a condenser, and a downcomer (liquid tube), and the condenser is higher than the evaporator by a certain vertical distance. The TPTL is powered by the natural force --- gravity, which means the pressure drop in the cycle always equals to the liquid head caused by the density difference of the liquid in the downcomer and the vapor or vapor/liquid mixture in the riser. Therefore, the liquid head is a key factor affecting the circulation flow rate and energy performance of TPTLs. In the traditional applications, the liquid head is considered to be proportional to the height difference between the condenser and the evaporator based on the underlying assumption: the downcomer is fully liquid filled. According to that, the TPTL will perform better with a larger height difference. The conclusion may be correct in the cases with large temperature difference and heat flux, such as in the field of cooling of electronic and light water reactors. However, when the TPTL is used in air-conditioning system, which has quite small temperature difference and small heat flux, some special phenomena were observed: the liquid heat is lower than the height difference and the downcomer is partially liquid filled. That is largely different from the thermosyphons in traditional applications. What’s the thermodynamic mechanism of partially liquid filled in the downcomer? How to determine the liquid head and the height difference? These are the fundamental questions that required answers before using two-phase thermosyphon in air conditioner field. In this study, the thermodynamic mechanism of partially liquid filled in the downcomer is researched and the effect of height difference on the performance of TPTL is investigated theoretically and experimentally. Firstly, a visual experimental setup is established, and the performance of a water-water TPTL is measured when the height difference ranges from 0 m to 2.4 m. Based on it, the basic phenomena are observed and the thermodynamic mechanism is investigated. Secondly, a generalized distributed-parameter model is developed based on the conservations of momentum, energy, and mass, which can determine liquid head and overall performance simultaneously according to external conditions. The model is verified by experiments. Then the model is used to analyze the variation of liquid head, circulation flow rate, heat transfer rate, system pressure under different height differences. The results show that with the increase of the height difference, the liquid head rises continuously until remain stable. Therefore, the liquid head is less than the height difference in some cases. Consequently, with the increase of height difference, the circulation flow rate and thermal performance firstly increases then remains constant

Outpainting by Queries

Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent inductive biases to achieve effective sample learning, which may degrade the performance ceiling. In this paper, motivated by the flexible self-attention mechanism with minimal inductive biases in transformer architecture, we reframe the generalised image outpainting problem as a patch-wise sequence-to-sequence autoregression problem, enabling query-based image outpainting. Specifically, we propose a novel hybrid vision-transformer-based encoder-decoder framework, named \textbf{Query} \textbf{O}utpainting \textbf{TR}ansformer (\textbf{QueryOTR}), for extrapolating visual context all-side around a given image. Patch-wise mode's global modeling capacity allows us to extrapolate images from the attention mechanism's query standpoint. A novel Query Expansion Module (QEM) is designed to integrate information from the predicted queries based on the encoder's output, hence accelerating the convergence of the pure transformer even with a relatively small dataset. To further enhance connectivity between each patch, the proposed Patch Smoothing Module (PSM) re-allocates and averages the overlapped regions, thus providing seamless predicted images. We experimentally show that QueryOTR could generate visually appealing results smoothly and realistically against the state-of-the-art image outpainting approaches

EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables

While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modelled with complex unknown partial differential equations (PDEs) which play a prominent role in many disciplines of science and engineering. In this paper, we propose a continuous-time model for arbitrary-step prediction to learn an unknown PDE system in multivariate time series whose governing equations are parameterised by self-attention and gated recurrent neural networks. The proposed model, \underline{E}xogenous-\underline{g}uided \underline{P}artial \underline{D}ifferential \underline{E}quation Network (EgPDE-Net), takes account of the relationships among the exogenous variables and their effects on the target series. Importantly, the model can be reduced into a regularised ordinary differential equation (ODE) problem with special designed regularisation guidance, which makes the PDE problem tractable to obtain numerical solutions and feasible to predict multiple future values of the target series at arbitrary time points. Extensive experiments demonstrate that our proposed model could achieve competitive accuracy over strong baselines: on average, it outperforms the best baseline by reducing $9.85\%$ on RMSE and $13.98\%$ on MAE for arbitrary-step prediction

Glycerol carbonate synthesis from glycerol and dimethyl carbonate using guanidine ionic liquids

A large number of surplus glycerol from the biodiesel production can be used as renewable feedstock to produce glycerol carbonate. In this paper, a series of guanidine-based ionic liquids were synthesized to catalyze the transesterification of glycerol and dimethyl carbonate. The tunable basicity and the anion-cation cooperative effect were responsible for the obtained results. The [TMG][TFE] showed the best activity turnover frequency (TOF) of 1754.0 h(-1), glycerol (GL) conversion of 91.8%, glycerol carbonate (GC) selectivity of 95.5%) at 80 degrees C with 0.1 mol% catalyst for 30 min. The reaction mechanism of the transesterification was also proposed. (C) 2017 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.</p

Glycerol carbonate synthesis from glycerol and dimethyl carbonate using guanidine ionic liquids

A large number of surplus glycerol from the biodiesel production can be used as renewable feedstock to produce glycerol carbonate. In this paper, a series of guanidine-based ionic liquids were synthesized to catalyze the transesterification of glycerol and dimethyl carbonate. The tunable basicity and the anion–cation cooperative effect were responsible for the obtained results. The TMGTFE showed the best activity (turnover frequency (TOF) of 1754.0 h ? 1 , glycerol (GL) conversion of 91.8%, glycerol carbonate (GC) selectivity of 95.5%) at 80 °C with 0.1 mol% catalyst for 30 min. The reaction mechanism of the transesterification was also proposed

Popping of g-C3N4 mixed with cupric nitrate: Facile synthesis of Cu-based catalyst for construction of CN bond

A novel strategy to synthesize copper-based nanoparticles supported on carbon nitride (C3N4) was developed by popping of mixture containing C3N4 and cupric nitrate. Characterizations such as X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) indicate that the structure of g-C3N4 maintained although a popping process occurred. High resolution transmission electronic microscopy (HRTEM) characterization illustrated that copper-based nanoparticles with diameter of < 1 nm were well distributed on g-C3N4. This kind of copper catalyst exhibits high catalytic activity and selectivity in arylation of pyrazole, a simple and effect strategy to construct CN bond in organic chemistry. According to the results of control experiments and characterizations, cuprous oxide should be the catalytic active phase in the supported coper-based catalyst. Keywords: CN coupling, N-arylation, Carbon nitride, Catalysis, Copper-based catalys

Entropy Generation Rate Minimization for Methanol Synthesis via a CO₂ Hydrogenation Reactor

The methanol synthesis via CO2 hydrogenation (MSCH) reaction is a useful CO2 utilization strategy, and this synthesis path has also been widely applied commercially for many years. In this work the performance of a MSCH reactor with the minimum entropy generation rate (EGR) as the objective function is optimized by using finite time thermodynamic and optimal control theory. The exterior wall temperature (EWR) is taken as the control variable, and the fixed methanol yield and conservation equations are taken as the constraints in the optimization problem. Compared with the reference reactor with a constant EWR, the total EGR of the optimal reactor decreases by 20.5%, and the EGR caused by the heat transfer decreases by 68.8%. In the optimal reactor, the total EGRs mainly distribute in the first 30% reactor length, and the EGRs caused by the chemical reaction accounts for more than 84% of the total EGRs. The selectivity of CH3OH can be enhanced by increasing the inlet molar flow rate of CO, and the CO2 conversion rate can be enhanced by removing H2O from the reaction system. The results obtained herein are in favor of optimal designs of practical tubular MSCH reactors