一种基于FCOS神经网络的小建筑物目标检测方法北大核心

提出了一种基于FCOS神经网络的小建筑物目标检测算法,针对FCOS模型在特征提取阶段提取到的小建筑物目标特征较少问题,引入多尺度检测和可变形卷积方式,加强网络对小建筑物目标的特征提取能力,并通过改进后的SGE注意力机制降低特征图中的干扰噪声权重。改进后的网络可以提取到更多的小建筑物目标特征,对环境干扰噪声的鲁棒性更强。在自己搭建的数据集上进行了实验测试,结果表明,在相同环境下网络改进后建筑物的整体检测准确率提升了1.7%,其中对小建筑物目标提升了3.6%,减少了小建筑物目标漏检、误检的问题。     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

基于多尺度的贝叶斯模型显著性检测

针对传统基于贝叶斯模型的显著性检测算法存在准确率不理想的问题,提出了一种基于多尺度的贝叶斯模型显著性检测算法。通过超像素分割算法(SLIC)将原图分割成不同尺度的超像素,根据超像素边界信息得到背景种子,进而通过距离计算和多尺度融合得到背景先验;对原图进行颜色增强,采用Harris算子对增强图进行检测角点求得凸包,融合不同尺度下的超像素得到凸包先验;融合背景先验和凸包先验得到最终先验;利用颜色直方图和凸包计算似然概率;将最终先验和似然概率通过贝叶斯模型计算显著图。在公开数据集MSRA1000、ECSSD上与多种传统算法进行准确率和召回率对比,该算法有更好的表现。     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.     

甘肃省陇东地区极端降水的时空特征

为阐明甘肃省陇东地区极端降水的变化规律,利用该地区13个气象台站1961~2015年的逐日降水资料,应用超门限峰值法、反距离加权法、线性倾向估计法、滑动平均法、Mann-Kendall突变分析法、复值Morlet小波分析法研究极端降水的时空变化特征。结果表明,陇东地区各站第95百分位极端降水阈值在27.8mm附近变化,即可以大雨事件作为极端降水事件进行监测;极端降水指数主要表现为从南向北递减的趋势,极端降水事件对环县和崆峒的影响较小;极端降水事件虽然在总量、强度上表现为减小趋势,但其发生频次及对总降水的贡献率逐渐增强;极端降水总量和极端降水强度周期变化均在21年左右能量最强,而极端降水频次和极端降水贡献率周期变化分别在28、5年左右能量最强。     

基于高分一号遥感卫星的渤海湾悬浮泥沙时空分布反演

悬浮泥沙是评价II类水体水质的重要参数之一,近海岸水域悬浮泥沙对重金属、营养盐等具有一定的吸附作用,悬浮泥沙的分布特征在一定程度上反映着污染物的迁移和循环,因此研究悬浮泥沙对渤海湾建设和环境保护具有重要意义。为探究渤海湾水域悬浮泥沙浓度 和分布的变化特征,利用高分一号多光谱卫星影像,通过采用波段组合的方式对悬浮泥沙含量指数反演提取,反演得到2019年9个月的渤海湾悬浮泥沙指数,分析发现渤海湾悬浮泥沙含量在冬季受季风影响较高,春季和夏季的时候渤海湾高浓度悬浮泥沙向近岸收缩,秋季 悬浮泥沙分布格局有正在向冬季过渡的趋势,本文研究可为渤海湾的生态环境保护和工业开发提供重要的科学依据和参考价值。     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

结合句法依存信息的方面级情感分类

引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。     

Study of spatial and temporal evolution of Ar and F atoms in SF6/Ar microsecond pulsed discharge by optical emission spectroscopy

The study of sulfur hexafluoride (SF₆) discharge is vital for its application in gas-insulated equipment. Direct current partial discharge (PD) may cause SF₆ decomposition, and the decomposed products of SF₆, such as F atoms, play a dominant role in the breakdown of insulation systems. In this study, the PD caused by metal protrusion defects is simulated by a needle-plate electrode using pulsed high voltage in SF₆/Ar mixtures. The spatial and temporal characteristics of SF6/Ar plasma are analyzed by measuring the emission spectra of F and Ar atoms, which are important for understanding the characteristics of PD. The spatial resolved results show that both F and Ar atom spectral intensities increase first from the plate anode to the needle and then decrease under the conditions of a background pressure of 400 Pa, peak voltage of −1000 V, frequency of 2 kHz, pulse width of 60 μs, and electrode gap of 5–9 mm. However, the distribution characteristics of F and Ar are significantly different. The temporal distribution results show that the spectral intensity of Ar decreases first and then increases slowly, while the spectral intensity of F increases slowly for the duration of the pulsed discharge at the electrode gap of 5 mm and the pulse width of 40–80 μs.