We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical ...linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments: robust because performance results with artificially generated matrices can be misleading, and repeatable because matrices are curated and made publicly available in many formats. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs). We provide software for accessing and managing the Collection, from MATLAB™, Mathematica™, Fortran, and C, as well as an online search capability. Graph visualization of the matrices is provided, and a new multilevel coarsening scheme is proposed to facilitate this task.
For multi-ceramic materials based on the stereolithography (SL) principle, a 3D printing strategy was developed, and then an Al2O3-Si3N4 functionally graded material (FGM) ceramic part was fabricated ...using this strategy. Six groups of mixtures, with a Si3N4 content gradient of 20 vol% and a certain bimodal particle size distribution, were prepared using UV-curable pastes. A modified formula was proposed to evaluate the relationship between the actual minimum voidage of mixtures and the viscosities of their corresponding pastes. The viscosity of each paste was controlled using the prediction formula and optimization of dispersants. To design theprinting layer thickness, a mathematical relationship was established between Si3N4 content and curing depth of paste. The Al2O3-Si3N4 green body without deformation was printed using optimized parameters such as a layer thickness of 40 μm and a paste viscosity of ∼13,000 mPa·s. Finally, using debinding and sintering, denseparts having a complicated shape were obtained.
We present the results of evaluating four techniques for displaying group or cluster information overlaid on node-link diagrams: node coloring, GMap, BubbleSets, and LineSets. The contributions of ...the paper are three fold. First, we present quantitative results and statistical analyses of data from an online study in which approximately 800 subjects performed 10 types of group and network tasks in the four evaluated visualizations. Specifically, we show that BubbleSets is the best alternative for tasks involving group membership assessment; that visually encoding group information over basic node-link diagrams incurs an accuracy penalty of about 25 percent in solving network tasks; and that GMap's use of prominent group labels improves memorability. We also show that GMap's visual metaphor can be slightly altered to outperform BubbleSets in group membership assessment. Second, we discuss visual characteristics that can explain the observed quantitative differences in the four visualizations and suggest design recommendations. This discussion is supported by a small scale eye-tracking study and previous results from the visualization literature. Third, we present an easily extensible user study methodology.
•Deep CNNs outperform OSTU and BTS methods in identifying water bodies from SAR imagery;•Speckle noise is suppressed by deep CNNs prior to the Refined Lee filter;•The summer flooding in 2020 of the ...Poyang Lake area, China, is monitored using Multiple CNNs.
Precise monitoring of floods is significant in disaster management and loss reduction; however, remote sensing data resource and methods can largely affect the monitoring accuracy of flooded areas. In this study, we use cloud-free Sentinel-1 Synthetic Aperture Radar (SAR) imagery, preferable to the optical imagery. We have used 5 convolutional neural networks (CNNs), including HRNet, DenseNet, SegNet, ResNet and DeepLab v3 + for flood monitoring in the Poyang Lake area, and compared their performances with the traditional methods — the bimodal threshold segmentation (BTS) and the OSTU method. The HRNet has superior performance in water body identification with the highest precision and efficiency, based on a parallel structure to not only extract rich semantic information but also maintain high-resolution features in the whole process. Besides, speckle noise reduction by deep convolutional neural networks in SAR imagery is better compared with the Refined Lee filter. The CNNs are then used to monitor the temporal evolution of summer flooding (May-Nov.) in 2020. Results show the smallest water coverage of Poyang Lake in late May; it gradually increases to the maximum in mid-July, and then shows a downward trend until November.
Here, the energy harvesting (EH) relaying node which needs to complete the simultaneous wireless information and power transfer (SWIPT) is considered. The EH relaying node uses the EH technique to ...get the energy supply and uses the directional transmission to improve the performance of SWIPT. The double discrete‐time‐switch (D‐DTS) based energy harvesting–power transfer–information transmission (EH–PT–IT) protocol is designed. One of the discrete‐time‐switch (DTS) controls the receiver of the EH relaying node and the other controls the transmitter. The performance is analysed by considering the average information rate and the average transferred power. The expressions of the average information rate and the average transferred power are derived analytically. The balanced maximization is considered, and the optimal parameters are obtained analytically. The simulations are presented to verify the analytical results and compare the performance. Comparing with previous works, the SWIPT performance using D‐DTS protocol in this work is better.
As the number of substations continues to increase globally and the market demand continues to rise, the current workload of maintenance and daily operation of substations in power grids cannot meet ...the current demand if only relying on manual work, and the design and implementation of intelligent safety control solutions for substations is imperative. Therefore, this paper proposes a joint safety control system and model analysis for substations based on multi-source heterogeneous data fusion. Firstly, a three-dimensional visualization substation efficient interactive operation platform is realized, which realizes the functions of substation scene roaming, system login, information management, equipment parameters, status viewing and operation ticket pushing; after that, a variety of intelligent hardware devices for data collection, such as multi-dimensional terminal sensors, intelligent wearable devices, intelligent pre-built positioning installation measure rod, and substation intelligent inspection robots are designed to greatly improve the substation inspection efficiency and realize real-time monitoring and data interaction in the inspection process. Finally, we propose an Attention-LSTM-based prediction model for substation multidimensional data, which can predict power equipment spatio-temporal data in the short term, and the prediction results can be combined with intelligent devices for joint diagnosis. The Attention-LSTM prediction model is well-trained in transformer oil temperature experiments, and the experimental results show that this model can provide early warning for the abnormal state of substation power equipment. In summary, this thesis describes a set of complete and practically feasible intelligent safety control methods for substations. The joint safety control system and model analysis of the substation based on multi-source heterogeneous data fusion designed in this paper is mainly oriented to the substation as an electric power workplace, which has quite a vast application prospect for energy equipment.
Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In ...this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.
Kernel development and starch formation are the primary determinants of maize yield and quality, which are considerably influenced by drought stress. To clarify the response of maize kernel to ...drought stress, we established well-watered (WW) and water-stressed (WS) conditions at 1-30 days after pollination (dap) on waxy maize (Zea mays L. sinensis Kulesh). Kernel development, starch accumulation, and activities of starch biosynthetic enzymes were significantly reduced by drought stress. The morphology of starch granules changed, whereas the grain filling rate was accelerated. A comparative proteomics approach was applied to analyze the proteome change in kernels under two treatments at 10 dap and 25 dap. Under the WS conditions, 487 and 465 differentially accumulated proteins (DAPs) were identified at 10 dap and 25 dap, respectively. Drought induced the downregulation of proteins involved in the oxidation-reduction process and oxidoreductase, peroxidase, catalase, glutamine synthetase, abscisic acid stress ripening 1, and lipoxygenase, which might be an important reason for the effect of drought stress on kernel development. Notably, several proteins involved in waxy maize endosperm and starch biosynthesis were upregulated at early-kernel stage under WS conditions, which might have accelerated endosperm development and starch synthesis. Additionally, 17 and 11 common DAPs were sustained in the upregulated and downregulated DAP groups, respectively, at 10 dap and 25 dap. Among these 28 proteins, four maize homologs (i.e., A0A1D6H543, B4FTP0, B6SLJ0, and A0A1D6H5J5) were considered as candidate proteins that affected kernel development and drought stress response by comparing with the rice genome. The proteomic changes caused by drought were highly correlated with kernel development and starch accumulation, which were closely related to the final yield and quality of waxy maize. Our results provided a foundation for the enhanced understanding of kernel development and starch formation in response to drought stress in waxy maize.
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