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文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法。通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置。提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器。这种状态观测器可以将暂态行为转换为由代数微分方程描述的广域电力系统,而无需非线性反演技术。最后以IEEE-5电力系统为例,说明了该方法的有效性。 相似文献
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Zeyu He Jiayue Han Xiaoyang Du Luye Cao Jun Wang Caijun Zheng Hui Lin Silu Tao 《Advanced functional materials》2021,31(37):2103988
Emerging graphene/organic phototransistors are eye-catching technologies owing to their unique merits including easy/low-cost fabrication, temperature independent, and achieving various functions. However, their development in the near-infrared (NIR) region is experiencing a bottleneck of inferior sensitivity due to low exciton dissociation efficiency and inefficient charge extraction rate. Here, a novel-design solution-processed graphene/organic NIR phototransistor is reported, that is, creatively introducing electron extraction layer of ZnO on graphene channel and employing organic ternary bulk heterojunction as photosensitive layer, successfully breaking that bottleneck. The phototransistor exhibits a high responsivity of 6.1 × 106 A W−1, a superior detectivity of 2.4 × 1013 Jones, and a remarkable minimum detection power of 1.75 nW cm−2 under 850 nm radiation. Considering its excellent NIR detection performance, a noncontact transmission-type pulse monitoring is carried out with no external circuit support, from which human pulse signal and heart rate can be displayed in real time. The phototransistor, interestingly, can be switched into a photomemory function with a retention time of 1000 s in the atmosphere through a gate voltage of −20 V. The design takes the characteristics of graphene/organic phototransistors to a higher level, beyond the limit of sensitivity, and opens up a novel approach for developing multifunction devices. 相似文献
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Tongling Xia Yue Qi Xilei Dai Jinyu Liu Can Xiao Ruoyu You Dayi Lai Junjie Liu Chun Chen 《Indoor air》2021,31(6):2020-2032
To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM2.5 monitoring using low-cost light-scattering sensors. 相似文献
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仲春华 《皮革制作与环保科技》2021,(5):34-35
社会经济的快速发展对生态环境造成了较大破坏,并产生了一系列连锁反应,不利于我国可持续发展目标的实现。为此,必须要切实做好环境监测工作。本文从环境监测的概念出发,就现阶段的环境监测现状进行了分析,并在此基础上阐述了环境监测工作未来发展及优化对策,以供参考。 相似文献
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With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications. 相似文献