We present a deterministic polynomial time algorithm to sample a labeled planar graph uniformly at random. Our approach uses recursive formulae for the exact number of labeled planar graphs with n vertices and m edges...
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(纸本)3540404937
We present a deterministic polynomial time algorithm to sample a labeled planar graph uniformly at random. Our approach uses recursive formulae for the exact number of labeled planar graphs with n vertices and m edges, based on a decomposition into 1-, 2-, and 3-connected components. We can then use known sampling algorithms and counting formulae for 3-connected planar graphs. (c) 2007 Elsevier B.V. All rights reserved.
In this paper,the problem of primary frequency regulation is investigated for large networks,including various AC areas interconnected by a multi-terminal high-voltage direct current(HVDC) *** control schemes are prop...
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In this paper,the problem of primary frequency regulation is investigated for large networks,including various AC areas interconnected by a multi-terminal high-voltage direct current(HVDC) *** control schemes are proposed to guarantee frequency consensus and adapted to the discrete situation in power grid *** first sampling algorithm modifies the power injections from each AC area into the DC grid as a function of sampling frequency deviations of neighbouring AC areas to share reserves among *** second intermittent sampling control has the capability of decreasing the working frequency of the sensors and controllers as an extension of the sampling *** both schemes,corresponding sufficient and necessary conditions for stabilizing the HVDC system asymptotically are further *** the numerical examples are given to illustrate the theoretical analysis.
The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data. Unfortunately, cities often...
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The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data. Unfortunately, cities often do not have or cannot release complete infrastructure data, and high-resolution terrain data products are not available everywhere. In this study, we quantify how the accuracy and precision of urban hydrologic hydrodynamic models vary as a function of data completeness and model resolution. For this aim, we apply the one-dimensional (1D) and coupled one-and two-dimensional (1D-2D) versions of the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM) in an urban catchment in the city of Phoenix, Arizona. Here, we have collected detailed infrastructure data, a high-resolution 0.3-m LiDAR-based digital elevation model, and catchment properties data. We tested several model configurations assuming different levels of (i) availability of stormwater infrastructure data (ranging from 5% to 75% of attribute-values missing) and (ii) terrain aggregation (i.e., 4.6 m and 9.7 m). These configurations were generated through random Monte Carlo sampling for SWMM 1D and selective sampling with four cases for SWMM 1D-2D. We ran simulations under the 50-year return period design storm and compared simulated flood metrics assuming the highest resolution and complete data model configuration as a reference. The study found that the model may over or underestimate flood volume and duration with different levels of missing data depending on the parameters - roughness, diameter or depth, and that model performance is more sensitive to missing data that is downstream and closer to the outfall as opposed to missing data upstream. Errors in flood depth, area and volume estimation are functions of both the data completeness and model resolution. Missing feature data leads to overestimation of flood depth, while lower model resolution results in underestimati
Image fusion is a classical problem in the field of image processing whose solutions are usually not unique. The common image fusion methods can only generate a fixed fusion result based on the source image pairs. The...
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Image fusion is a classical problem in the field of image processing whose solutions are usually not unique. The common image fusion methods can only generate a fixed fusion result based on the source image pairs. They tend to be applicable only to a specific task and have high computational costs. Hence, in this paper, a two-stage unsupervised universal image fusion with generative diffusion model is proposed, termed as UUDFusion. For the first stage, a strategy based on the initial fusion results is devised to offload the computational effort. For the second stage, two novel sampling algorithms based on generative diffusion model are designed. The fusion sequence generation algorithm (FSGA) searches fora series of solutions in the solution space by iterative sampling. The fusion image enhancement algorithm (FIEA) greatly improves the quality of the fused images. Qualitative and quantitative evaluations of multiple datasets with different modalities demonstrate the great versatility and effectiveness of UUD-Fusion. It is capable of solving different fusion problems, including multi-focus image fusion task, multi-exposure image fusion task, infrared and visible fusion task, and medical image fusion task. The proposed approach is superior to current state-of-the-art methods. Our code is publicly available at https://***/xiangxiang-wang/UUD-Fusion.
The use of mathematical models for design space characterization has become commonplace in pharmaceutical quality-by-design, providing a systematic risk-based approach to assurance of quality. This paper presents a me...
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The use of mathematical models for design space characterization has become commonplace in pharmaceutical quality-by-design, providing a systematic risk-based approach to assurance of quality. This paper presents a methodology to complement sampling algorithms by computing the largest box inscribed within a given probabilistic design space at a desired reliability level. Such an encoding of the samples yields an operational envelope that can be conveniently communicated to process operators as independent ranges in process parameters. The first step involves training a feed-forward multi-layer perceptron as a surrogate of the sampled probability map. This surrogate is then embedded into a design centering problem, formulated as a semi-infinite program and solved using a cutting-plane algorithm. Effectiveness and computational tractability are demonstrated on the case study of a batch reactor with two critical process parameters.
When kernel methods are applied to detect the defection, there is a need to select the training samples, because kernel methods are based on the statistical learning theory. To extract the defects, the pre-image is ca...
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When kernel methods are applied to detect the defection, there is a need to select the training samples, because kernel methods are based on the statistical learning theory. To extract the defects, the pre-image is calculated. In this paper, a sampling algorithm based on the alignment is designed to improve the calculation efficiency, where kernel alignment can measure the similarity between different kernel functions and matrices. A local linear algorithm is proposed to calculate the pre-image. When obtain the 0–1 difference image, an algorithm is designed to determine whether there are defects. An algorithm is designed to calculate the center coordinates and the areas of defects in the 0–1 image. Using this method, the accuracy of detection can be improved, because the method can remove the effect from recovery errors. When using the algorithms on a data set of printing products, the experiment results show that the detection results are more accurately than using the difference matrix.
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