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微分代数方程的解算方法是电力系统仿真技术的基础，不同的解算方法得到仿真模型的解算结果之间存在差异，这对模型的计算分析及评估有重要影响。基于当前较为常用的商用软件平台中使用的三种分析方法—EMTP类软件的节点分析法、用于Matlab/Simulink的状态空间法以及用于RT-LAB平台的状态空间节点法，结合各算法的特性，基于典型RLC电路模型进行仿真，通过仿真结果说明各算法之间的差异，并理论分析了结果差异产生的原因。

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Unmanned aerial vehicle (UAV) aeromagnetic measurement, as an efficient, convenient and low-cost geophysical exploration method, has been widely used in geological survey, mineral resources exploration, and engineering prospecting. First, we outline the historical process of UAV aeromagnetic measurement systems in the past two decades. The domestic and international progress of research and development, and the application of UAV aeromagnetic measurement are summarized. We compare the advantages and disadvantages of three main types of UAV flight platforms and their application scenarios in the field of aeromagnetic measurement. The types and characteristics of UAV aerial magnetometers are analyzed. Second, we carefully discuss the interference problem of aeromagnetic measurement. The principle of aeromagnetic compensation is mathematically described. The applications of various aeromagnetic measurement compensation methods are discussed. The characteristics of UAV electromagnetic interference and related suppression methods are summarized. The relevant ways to further improve the accuracy of UAV aeromagnetic measurement on the basis of traditional magnetic compensation are pointed out. Finally, the typical application cases of UAV aeromagnetic measurement technology are shown and the challenges and developments of UAV aeromagnetic measurement technology are foreseen. [ABSTRACT FROM AUTHOR]

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Aiming at the difficult problems of multi-source data fusion in e-health service management, a multi-task support vector machine for data fusion (m S V M D F) method, which combines multi-task learning theory and support vector machine theory from the perspective of artificial intelligence, is proposed. In m S V M D F method, the data fusion model is obtained by the feature vector having the same type data source based on support vector machine theory, and both structure sparsity and tasks dependency is considered under the multi-task learning framework, to achieve multi-source data fusion with different number of data sources. Extensive multisource images and examination data fusion experiments are conducted to reveal the performance of m S V M D F method. The experimental results show that the proposed method can effectively overcome the difficulties of multi-source data fusion with different number of data sources and has good classification performance and structure sparsity. [ABSTRACT FROM AUTHOR]

Copyright of Qing History Journal is the property of Renmin University of China, Institute of Qing History and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In many Chinese metropolitan areas, in addition to normal traffic uncertainties, the operability of each vehicle in the fleet can be uncertain due to randomly occurring severe weather condition (e.g., smog), which leads to imposition of vehicle operational restriction. Considering these uncertainty sources, we propose a 2-stage stochastic integer programming model for the strategic-level delivery fleet planning problem, to ensure the coordination of daily production and delivery operations. The model aims to minimize the strategic-level procurement spending on delivery fleet and the expected operational cost under logistic uncertainty. In order to solve this problem, we adapt a stochastic branch-and-bound (SBB) method, for which a local search heuristic is applied to improve the efficiency of the upper bounding. We also conduct the numerical study to provide the model validity and algorithm viability. Compared to solving the expected value problem, our 2-stage stochastic integer programming model can modestly reduce the average daily operating cost. Finally, we report sensitivity analysis results on how the fleet planning decisions would be affected by different types of vehicle operational restrictions with varying occurrence frequencies. This study offers a solution for city delivery problems caused by the vehicle operational restrictions, which is helpful for improve the daily operating performance of city logistics industry. [ABSTRACT FROM AUTHOR]

Motivated by the phenomena in intelligent electronics supply chains, we study capacity/output decision-making in a supply chain with limited capacity. Based on game theory and constrained extreme value theory, we build a three-stage game model, characterize the corresponding equilibrium and the profit division amongst the supply chain. We also conduct sensitivity analysis to see the effect of the parameters. We find that the supplier (also as a competitor to the buyer) may leverage the limited capacity to push the buyer out of the market, while an additional buying option does not necessarily neutralize the supply shortage threat for the buyer. Our conclusions can provide meaningful managerial insights with respect to the quantity competition in intelligent electronics supply chains. [ABSTRACT FROM AUTHOR]

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