光电转台轻量化几何形状优化研究

对光电转台进行轻量化,首先需要进行几何形状进行优化.筋板如何布置才能使转台的动态刚度达到最优,在设计中一直是一个难点.基于此,通过对转台的连接部件、止推轴承及径向轴承进行有效简化,建立起了光电转台的动力学模型,结合模型.对转台内部的筋板布置形式进行了详尽的研究比较.研究结果表明,要想获得满意的动态刚度,单纯地增加筋板数量并不能达到要求,而应该根据其具体的振动情况来选择合理的筋板布置形式,并且在平行于振型方向的筋板上,开孔时的动刚度效果相对地比未开孔时的动刚度效果要好.     

基于背景模型的运动目标检测与跟踪

运动目标检测与跟踪是判断视频序列中是否存在运动目标及监控运动目标时空变化和运动轨迹.基于背景模型的运动目标检测与跟踪是有效的手段,设定阈值对当前帧图像与已有背景图像相减的差分图像进行检测跟踪操作.检测准确且速度快,阈值操作后直接给出运动目标的位置、大小、形状等信息.     

基于ROC曲线的水印算法可靠性分析

分析讨论了水印算法的可靠性问题,首次将ROC曲线应用到水印算法的可靠性评价中,从而可以动态的、利用变化的门限分析各种水印算法的可靠性,弥补了传统测评方案中的不足,为公正、客观地评价水印算法的可靠性提供了有利依据.     

考虑天然气混氢的园区综合能源系统电制氢优化配置

电制氢和天然气混氢技术在促进可再生能源消纳、降低系统碳排放量方面具有良好的理论研究和工程应用前景。面向含高比例可再生能源的园区综合能源系统,提出一种计及天然气混氢及跨季节存储的电制氢优化配置方法。首先梳理了含氢园区综合能源系统的运行框架和能量流动关系,建立园区内部能源生产、转换与存储设备的数学模型,其次以设备的年化投资成本、园区综合能源系统的年度运行成本和碳交易成本最优为目标,提出电制氢优化配置模型。最后通过算例分析表明电制氢及天然气混氢技术的引入可提升可再生能源的消纳能力,降低系统的整体经济成本和碳排放量,并分析了电解槽投资成本、混氢体积分数上限以及经济性和低碳性成本权重系数变化对规划运行结果的影响。关键词：园区综合能源系统;电制氢;天然气混氢;碳交易;跨季节储氢;优化配置 中图分类号：TM732     

视觉对比度机制小目标增强与背景抑制综述北大核心CSCD

复杂背景中的红外弱小目标因亮度低、尺寸小、可用特征少而难以检测。如何在检测中抑制背景杂波、提高目标信噪比成为该领域的研究热点与难点。本文对基于人类视觉系统对比度机制的小目标增强与背景抑制技术的演进和性能进行了归纳与分析。局部对比度测度窗口由单尺度向多尺度,乃至动态或自适应尺度的发展,满足应用中对未知尺寸小目标同步快速检测的需要;局部对比度测度计算方法由简单到复杂、依据低阶信息到采用高阶信息的变化,有利于更全面地抑制复杂背景、进一步增强目标。因此,将成为未来人类视觉对比度机制小目标检测算法的发展方向。     

区块链技术在多元融合高弹性电网中的应用初探

多元融合高弹性电网通过构建源网荷储全向交互机制,显著提升了电网的互动与自愈能力。区块链技术采用去中心化理念和分布式记账模式,保障数据可信,打破信任壁垒,为电网多端友好互动提供了核心技术支撑。基于现有理论研究与工程实践,区块链的技术特征与高弹性电网的形态架构具有较强的理念契合性和技术互补性,区块链有潜力成为多元融合高弹性电网的重要赋能技术。面向多元融合高弹性电网的建设目标,刻画了区块链技术与高弹性电网深度融合的应用场景,梳理了建设高弹性电网过程中的关键痛点,阐明了区块链之于高弹性电网的技术赋能要素及匹配关系。面向高弹性电网的建设需求,提出了区块链系统的设计原则、评价指标以及设计流程,并以需求响应为例进行系统设计与性能测试,为区块链技术在高弹性电网中的应用提供理论支撑。     

Weakly supervised temporal action localization with proxy metric modeling

Temporal localization is crucial for action video recognition. Since the manual annotations are expensive and time-consuming in videos, temporal localization with weak video-level labels is challenging but indispensable. In this paper, we propose a weakly-supervised temporal action localization approach in untrimmed videos. To settle this issue, we train the model based on the proxies of each action class. The proxies are used to measure the distances between action segments and different original action features. We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds. Compared with state-of-the-art methods, our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.     

Exploring the tidal effect of urban business district with large-scale human mobility data

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.     

Label distribution learning for scene text detection

Recently, segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts. However, existing methods usually need complex post-processing stages to process ambiguous labels, i.e., the labels of the pixels near the text boundary, which may belong to the text or background. In this paper, we present a framework for segmentation-based scene text detection by learning from ambiguous labels. We use the label distribution learning method to process the label ambiguity of text annotation, which achieves a good performance without using additional post-processing stage. Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.     

计及可靠性收益的配电网柔性多状态开关及分布式电源综合优化配置

柔性多状态开关(flexible multi-state switch,FMS)是支撑新型电力系统背景下配电网运行控制能力提升和可再生能源接入渗透率改善的重要技术装备,针对现有配电网中FMS的规划并未详细考虑FMS与分布式电源(distributed generation,DG)的协调配置,以及对于FMS接入带来的可靠性提升考虑不足的问题。文章在规划配置阶段详细考虑了含FMS和DG的协同优化运行,并充分计及FMS接入带来的可靠性收益,构建了基于嵌套优化的FMS和DG选址定容双层优化配置模型。针对双层嵌套规划模型求解的复杂性,提出了基于改进粒子群算法和GAMS(general algebraic modeling language)算法相结合的模型求解策略,以改造后的IEEE 33节点算例系统为例,对所提出模型和方法的有效性进行了验证,结果表明提出的方法相比传统单一FMS规划具有更好的经济性,能最大程度地降低配电网年综合费用,同时减少用户因停电带来的损失。