学术期刊同行评议进度跟踪的优化策略研究

学术期刊同行评议进度跟踪有利于编辑部提前发现审稿异常稿件并及时处理,对同行评议周期控制具有直接影响.本文通过问卷调查和个案数据揭示进度跟踪的现状及问题.结果显示:1)期刊对同行评议进度跟踪主要依赖系统提醒,但大多期刊仅将其设置在临近审稿期限;超过一半的编辑几乎不进行人工跟踪.2)72.86％的编辑虽然要求专家回复审稿邀...     

基于HowNet的话题跟踪及倾向性分类研究

本文研究了如何基于信息检索技术和“知网”实现有效的话题跟踪和话题立场分类。话题跟踪任务就是给出话题相关的训练新闻报道,系统在后续报道中发现与这个话题相关的报道。它属于话题检测与跟踪的一项子任务。本文针对跟踪任务中话题本身的特点,论述了权重调整、事件框架和报道扩充等多种提高跟踪性能的策略,同时基于“知网”中的情感体系和动态角色框架,提出了如何填充框架并结合建立的立场概念库对报道进行话题立场分类。实验证明这些方法是有效的。     

一种提高室内定位精度的模糊逻辑方法

超宽带技术由于其精确的定时分辨率,在视距（LOS）和非视距（NLOS）混合环境中应用前景广阔。为提高跟踪精确度、消除与UWB跟踪系统测距数据集异常值,构建合适的误差抑制方法,提出一种误差抑制方法。首先对环境进行建模,分析LOS和NLOS环境中的信号参数;然后,采用基于模糊逻辑的两步自适应定位算法,分析和选择最佳测距数据,用于计算被跟踪设备的位置;使用模糊加权最小二乘估计（FWLSE）方法对非视距（NLOS）混合环境中的实际数据进行测试。实验结果表明,与其它已知算法相比,该方法定位精度显著提高18.28%,且在实际应用中易于实现。     

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.     

基于安卓系统的手机防盗取证系统

手机防盗取证系统就是针对Android系统的智能手机,利用Android广播机制,当手机用户开启防盗取证后,系统将从后台监听手机,通过短信的方式给被控手机发送指令,实现定时录音、加密存储并上传至邮箱,抓拍照片、加密存储并上传至邮箱,获取最新通话记录和GPS定位功能,达到了手机防盗取证的功能,为公安人员提供了相应线索。     

Algorithmic skin: health-tracking technologies,personal analytics and the biopedagogies of digitized health and physical education

The emergence of digitized health and physical education, or ‘eHPE’, embeds software algorithms in the organization of health and physical education pedagogies. Particularly with the emergence of wearable and mobile activity trackers, biosensors and personal analytics apps, algorithmic processes have an increasingly powerful part to play in how people learn about their own bodies and health. This article specifically considers the ways in which algorithms are converging with eHPE through the emergence of new health-tracking and biophysical data technologies designed for use in educational settings. The first half of the article provides a conceptual account of how algorithms ‘do things’ in the social world, and considers how algorithms are interwoven with practices of health tracking. In the second half, three key issues are articulated for further exploration: (1) health tracking as a ‘biopedagogy’ of bodily optimization based on data-led and algorithmically mediated understandings of the body; (2) health tracking as a form of pleasurable self-surveillance utilizing data analytics technologies to predict future bodily probabilities and (3) the ways that health-tracking produces a body encased in an ‘algorithmic skin’, connected to a wider ‘networked cognitive system’. These developments and issues suggest the need for greater attention to how algorithmic systems are embedded in emerging eHPE technologies and pedagogies.     

基于移动闭塞原理的列车追踪模型及追踪间隔研究

介绍了移动闭塞原理,讨论了列车追踪运行常规模型并给出了追踪间隔的计算方法,在常规模型的基础上,提出了一种考虑前车位置与速度的新的追踪模型。结合具体线路数据进行仿真试验,将常规模型与新模型的试验结果进行比较与分析,根据仿真计算发现新的追踪模型大大减小了列车的追踪间隔。     

改进的强跟踪卡尔曼滤波器在MSINS/GPS组合导航中的应用研究

通过深入分析次优渐消因子的解算原理,提出了一种无须先验知识的多时变渐消因子估计方法,用于改进强跟踪卡尔曼滤波算法,并将其应用于MSINS/GPS浅组合中。采用多个次优渐消因子,分别对不同的数据通道进行渐消,可以有效提高滤波算法的跟踪能力。仿真结果表明,改进的强跟踪卡尔曼滤波算法解决了量测相关、初值选取敏感性等问题,可以提高系统的实时性、鲁棒性。     

TLD视频目标跟踪方法研究

介绍TLD（Tracking-Learning-Detection）的框架和原理,将TLD跟踪算法分解为跟踪、检测和学习三个部分,分析TLD目标跟踪算法,总结国内外TLD研究的进展和应用方向,具体分析TLD的不足和优越性,并简单预测和分析未来TLD目标跟踪算法研究的可能方向和应用前景.     

面向社交网热点话题传播的有影响力参与者追踪方法研究

在社交网络媒体上,热点话题由一系列相互联系的事件及其互动信息引起,追踪参与热点话题传播的负能量贡献者,对监测、预警和管控网络舆情具有重要的价值。本文将网络社区话题事件以及其成员之间互动关系映射为社交网络,运用社会性网络分析、离差最大化和类熵距离等方法,计算参与事件交互成员的聚焦指标及其权重、类熵距离、优劣度以及贡献率数值,从而在互联网中找出参与热点话题传播的能量贡献最大者,并运用实证方法验证本文方法的合理有效性。