Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.     

FS-Net: 面向时序知识图谱推理的频次统计网络

时序知识图谱推理吸引了研究人员的极大关注.现有的时序知识图谱推理技术通过建模历史信息取得了巨大的进步.但是,时变性问题和不可见实体(关系)问题仍然是阻碍时序知识图谱推理模型性能进一步提升的两大挑战;而且由于需要对历史子图序列的结构信息和时间依赖信息进行建模,传统的基于嵌入的方法往往在训练和预测过程中具有较高的时间消耗,这极大地限制了推理模型在现实场景中的应用.针对以上困境,提出了一个用于时序知识图谱推理的频次统计网络, FS-Net.一方面, FS-Net不断基于最新的短期历史的事实频次统计,动态地为变化的时间戳上的预测生成时变的得分;另一方面,FS-Net基于当前时间戳上的事实频次统计,为预测补充历史不可见实体(关系);特别地,FS-Net不需要进行训练,而且具有极高的时间效率.在两个时序知识图谱基准数据集上的大量实验,表明了FS-Net相较于基准模型的巨大提升.     

基于粗糙集的电池储能电站海量数据处理方法

锂离子电池的储能电站在运行过程中上传的数据量庞大,数据采样率高,增大了实时在线评估工作的难度。如何提取并压缩有效数据,同时保证压缩后的数据具有保真性,成为数据挖掘预处理所面对的主要问题。针对上述问题,选取储能电站特定工况下一个电池簇的数据,利用粗糙集方法,对数据的属性进行约简,根据正态分布的$ 2\sigma $原则对属性值进行二元逻辑划分,将单体电池划分为频繁检测对象和普通检测对象,压缩了在线处理的数据量。最后,对同一电池簇20天内不同工况数据进行验证,证明了该处理方法的有效性。     

基于FPGA的短波红外图像灰度级拉伸算法实现

为了增强短波红外成像仪的成像对比度,提高目标的识别率,介绍了一种基于现场可编程门阵列(FPGA)的灰度拉伸算法的实现方法。利用视频数据两帧之间灰度分布近似的特性,通过统计上一帧图像的灰度分布,计算图像拉伸所需要的参数,处理当前帧的图像,达到实时处理的效果。在灰度统计模块中,利用FPGA的片上块随机存储器(Block RAM)资源,采用非倍频的流水线数字逻辑设计,避免了跨时钟域的操作,降低了系统状态机的复杂度,提高了系统的工作频率。采用国产320×256元InGaAs面阵探测器,搭载了Xilinx Artix-7系列芯片的实验平台进行实验,仿真结果表明,该方法能有效提高短波红外图像的对比度,具有占用资源少、运算速度快、成本低、可移植性高等优点,满足短波红外成像仪实时灰度拉伸处理的设计要求。     

基于能量自采集的无线传感器网络网关切换机制研究

针对野外传感网系统中采用太阳能供电的网关因天气变化而产生能量供给失效的问题,研究了基于历史能量采集信息和实时气象信息的多网关切换方法。首先基于气象信息决定需要切换的网关和网关切换的时机;其次提出了一种网关选择算法(Easi GS),根据候选网关剩余工作时间让节点先验式选择网关接入,以避免网关失效带来的数据丢失问题,并通过最优网关接入方法降低系统中采集节点的整体能耗;最后根据实际应用关注的数据发送频率、网关恢复时间、节点与候选网关之间的传输距离等信息,通过概率统计的方法对Easi GS的计算开销进行了进一步优化。仿真实验表明,Easi GS能使系统整体能耗达到最优,并且优化后的Easi GS能够有效降低节点上的计算量。     

Revisiting Statistical Design and Analysis in Scientific Research

Statistics is essential to design experiments and interpret experimental results. Inappropriate use of the statistical analysis, however, often leads to a wrong conclusion. This concept article revisits basic concepts of statistics and provides a brief guideline of applying the statistical analysis for scientific research from designing experiments to analyzing and presenting the data.     

汽车铸件生产工时数理统计计算方法

采用多元非线性回归分析方法 ,对我国汽车铸造生产管理数据进行微机辅助分析 ,得出了铸件生产工时计算公式 ,编制了计算机辅助管理程序。     

一个在Horn子句中求解极大缩减的算法

在信念修正理论中,一个核心问题是求解一个公式集合关于事实集合的所有极大协调子集,即极大缩减.本文尝试从算法的角度来解决这一问题,研究在Horn子句中求解所有极大缩减的算法.首先,本文指出并证明了公式集合和事实集合并集的极小不协调子集与公式集合关于事实集合的极大缩减之间的转化关系.其次,给出并证明了Horn子句集合极小不协调的一个必要条件.然后,基于上述两个结论,本文提出了一个在Horn子句中枚举公式集合和事实集合并集的极小不协调子集的交互式算法和一个通过这些极小不协调子集计算所有极大缩减的算法.最后,综合这两个算法,提出了一个在Horn子句中求解所有极大缩减的交互式算法.     

An algorithm to compute maximal contractions for Horn clauses

In the theory of belief revision, the computation of all maximal subsets (maximal contractions) of a formula set with respect to a set of facts is one of the key problems. In this paper, we try to solve this problem by studying the algorithm to compute all maximal contractions for Horn clauses. First, we point out and prove the conversion relationship between minimal inconsistent subsets of union of the formula set and the set of facts and maximal contractions of the formula set with respect to th...     

Information fusion for automotive applications – An overview

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.