The fraction of mulched film is of great significance for evaluating the agricultural water-saving effect and controlling environmental plastic pollution. Unfortunately, there is no work has been done to obtain this parameter due to the mixed pixel issue of satellite imagery with medium and low resolutions, till now. In this study, we proposed a novel two-step framework for mapping fraction of mulched film based on very high resolution satellite observation and deep learning. The first step is extracting the extent of the mulched film based on a new few-shot learning model named PT-CNN (Parameter Transition Convolutional Neural Network), which aims to increase the extraction accuracy and overcome the lack of labeled training data. The second step is retrieving the fraction of the mulched film at pixel scale based on a spectrum analysis method. The result shows that the proposed PT-CNN outperforms several state-of-the-art methods in mulched film extraction, with F1-scores at 97.09% and 98.65% for white and black mulched film, respectively. Meanwhile, the retrieved pixel scale fraction of mulched film shows high consistency to in-situ measurement, with a MAE of 0.0321. The proposed method can be useful in agricultural water resource management and environmental governance.

Accurately predicting the battery cycle life of lithium-ion batteries in the early-cycle stage can provide a basis for long-term planning, bring economic benefits and avoid safety risks. However, it is very difficult to accurately predict the cycle life due to the weak degradation of battery performance in the early cycle stage. In this paper, an early stage prediction model of lithium-ion battery based on convolutional long short-term memory (ConvLSTM) with attention mechanism is proposed, which is called ConvLSTM-Attention model. ConvLSTM can not only extract the characteristics of single cycle information, but also mine the temporal relationship among each cycle data. For the features extracted by ConvLSTM, the attention mechanism is added, so that the model can pay attention to the important features and thus improve the prediction accuracy of the model. Experiments show that the model can predict the battery cycle life only by using the information of the first 10 cycles of the battery, and the model can predict whether the battery belongs to high-lifetime or low-lifetime only by using the information of the first 5 cycles of the battery. Comparison with other early prediction models show that the proposed model can achieve better prediction results by using less cycle data.

Soil moisture (SM) plays an important role in the water–heat–energy exchange and water cycle of the land ecosystem. Long-term SM products are vital in the time series study of ecology and hydrology. Therefore, it is vital to extend the time span with limited SM monitoring sensors, since there is no single long-term SM product currently. In this study, an SM product prolonging method based on an artificial neural network (ANN) and moderate-resolution imaging spectroradiometer (MODIS) optical products was proposed. The prolonging results of Soil Moisture Active Passive (SMAP) and Fenyun-3B (FY3B) products were validated in Tibetan Plateau to present the feasibility of this method. The result shows this method is feasible in areas under medium vegetation cover (0.2$R $= 0.84, RMSE3${cm}^{-3}$) within situmeasurements for both SMAP and FY-3B products. The generated long-term SM will benefit the global water cycle study.

Highly quality soil moisture is significant for hydrological, meteorological and agricultural applications. At present, active and passive remote sensing are the only ways to monitor soil moisture directly at regional scale. However, the quality of single satellite-based soil moisture product is insufficient to meet the requirements of these applications. Hence, fusing these two soil moisture products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive soil moisture products. First, the method is improved in finding the best instrumental variables in time series based on correlation coefficient. Second, fused weights of input soil moisture products are estimated using the improved method. Finally, fused soil moisture products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau was selected as the study area to test the algorithm using both of the Climate Change Initiative (CCI) active and passive soil moisture products from the European Space Agency (ESA). The ground validation results show that, compared with the original soil moisture products, the change capture ability, expressed by the correlation coefficient (R), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have been both improved by about 10% on average. This study indicates that the proposed fusion method can effectively improve the quality of soil moisture products to further understand the global changing water cycle.

Fast analysis of power grid networks has been a challenging problem for many years. The huge size renders circuit simulation inefficient and the large number of inputs further limits the application of existing Krylov-subspace macromodeling algorithms. However, strong locality has been observed that two nodes geometrically far have very small electrical impact on each other because of the exponential attenuation. However, no systematic approaches have been proposed to exploit such locality. In this paper, we propose a novel modeling and simulation scheme, which can automatically identify the dominant inputs for a given observed node in a power grid network. This enables us to build extremely compact models by projecting the system onto the locally dominant Krylov subspace corresponding to those dominant inputs only. The resulting simulation can be very fast with the compact models if we only need to view the responses of a few nodes under many different inputs. Experimental results show that the proposed method can have at least 100X speedup over SPICE-like simulations on a number of large power grid networks up to 1M nodes.

In this paper, a novel method to efficiently reduce the terminal number of general linear-interconnect circuits with a large number of input or output terminals considering delay uncertainty is proposed. Our new algorithm is motivated by the fact that terminal reduction can lead to a more compact order-reduced model and the observation that very large-scale integration interconnect circuits have many similar terminals in terms of their timing and delay metrics due to their closeness in structure or due to the mathematical discretization using meshing in finite-difference or finite-element scheme during the extraction process. The new method, called TermMerg ( Proc. ICCAD, p. 821, 2005), is based on the moments of the circuits as the metrics for the timing or delay. It then employs a singular-value-decomposition (SVD) method to determine the best number of clusters based on the low-rank approximation. After this, the -means clustering algorithm is used to cluster the moments of the terminals into the different clusters. The proposed method can work with any passive-model order reduction and ensure the passive models. In contrast, we show that singular value decomposition model order reduction (SVDMOR) does not generate passive models in general. Passivity enforcement in SVDMOR will significantly hamper the terminal-reduction effectiveness. Experimental results on a number of real industry interconnect circuits demonstrate the effectiveness of the proposed method and show also that the proposed method is more accurate than SVDMOR when the used moment matrix does not give good terminal correlations.

This paper proposes a fast decoupling capacitance (decap) allocation and budgeting algorithm for both early stage decap estimation and later stage decap minimization in today's very large scale integration physical design. The new method is based on a sensitivity-based conjugate gradient (CG) approach. But several new techniques that significantly improve the efficiency of the optimization process were adopted. First, an efficient search step scheme to replace the time-consuming line search phase in the conventional CG method for decap budget optimization was proposed. Second, instead of optimizing an entire large circuit, the circuit is partitioned into a number of smaller subcircuits and optimized separately by exploiting the locality of adding decaps. Third, the time-domain merged adjoint method was applied to compute the sensitivity information and show that the partitioning-based merged adjoint method leads to better results than the flat merged adjoint method with the improved search scheme. Experimental results show that the proposed algorithm achieves at least ten times speed-up over similar decap allocation methods reported so far with similar budget quality, and a power grid circuit with about one million nodes can be optimized using the new method in half an hour on the latest Linux workstations

This paper proposes a novel hierarchical approach to the efficient analysis of large VLSI power/ground grids. Different from the existing hierarchical approach where sub-circuit equivalent models are sparsified with computation-intensive integer programming and the resulting modeling may lead to larger errors if the top circuit matrix has large condition number, the new approach employs an iterative (relaxation) procedure to explicitly compensate the errors and avoid introducing dense matrix caused by the circuit reduction. We also propose an efficient scheme for partitioning high performance center-bumped P/G grids. Experimental results demonstrate that the new algorithm is more accurate than the existing hierarchical method while delivering more speedup over the flat simulators.

This paper proposes a fast decoupling capacitance (decap) allocation and budgeting algorithm for both early stage decap estimation and later stage decap minimization in today's VLSI physical design. The new method is based on a sensitivity-based conjugate gradient (CG) approach. But it adopts several new techniques, which significantly improve the efficiency of the optimization process. First, the new approach applies the time-domain merged adjoint network method for fast sensitivity calculation. Second, an efficient search step scheme is proposed to replace the timeconsuming line search phase in conventional conjugate gradient method for decap budget optimization. Third, instead of optimizing an entire large circuit, we partition the circuit into a number of smaller sub-circuits and optimize them separately by exploiting the locality of adding decaps. Experimental results show that the proposed algorithm achieves at least 10X speed-up over the fastest decap allocation method reported so far with similar or even better budget quality and a power grid circuit with about one million nodes can be optimized using the new method in half an hour on the latest Linux workstations.