Optical design of the Lyman alpha based beam emission spectroscopy(LAB)diagnostic on the HL-2A tokamak

In this article, we present the optical design of a novel diagnostic on the HL-2 A tokamak, i.e. the20-channel edge Lyman-alpha beam emission spectroscopy, which is a promising solution for edge density turbulence research on tokamaks, as it offers the possibility of density fluctuation measurement with a 3.3 mm spatial resolution while maintains a high temporal resolution of1 μs. The optical path, including the reflective collection optics, the high-dispersion spectrometer, and the linear detector array, is carefully optimized to obtain a good image quality and a high throughput. The maximum root mean square radius of the collection optics is 64 μm.The detected photon flux is estimated to be about 10~(11) photons/s/channel.     

Pricing and personal data collection strategies of online platforms in the face of privacy concerns

Electronic Commerce Research - We consider a platform providing free content for users and earning profit from the sale of advertising. The platform can collect and analyze personal data to...     

EMS数据流管理系统的框架设计

针对现有关系型数据库系统在能量管理系统（EMS）实时应用中存在的缺陷，设计了一个新型EMS数据流管理系统的框架，并建立了相对应的数据流模型。该框架能够根据流速不同对采集数据进行分类处理，通过数据流查询窗口对相量测量单元（PMU）高速数据进行分割、降速、筛选，实现了对PMU数据和原有监控与数据采集（SCADA）系统测量值的并行实时处理，并且能够在实现稳态计算功能的同时对系统进行动态监测。文中描述了系统的基本结构、各组成部分的功能和关键技术，并在数据流处理方面提出了一些提高效率的改进措施。     

颗粒活性炭在饮用水深度处理中的应用

概括介绍了国内外活性炭水处理的发展和应用情况、活性炭深度处理的机理及处理效果。针对当前水源有机污染现状和发展趋势 ,论述了采用活性炭进行饮用水深度处理的必要性 ,给出了颗粒活性炭吸附池设计的参数及要求。最后介绍了北京市城子水厂炭吸附池设计及特点。     

正电子湮没技术在研究合金形状记忆性能中的应用

本文用正电子湮没技术测试了Cu-Zn-Al形状记忆合金100℃母相时效过程中空位的变化。结果发现,随着时效时间的延长,空位浓度不断降低,空位尺寸不断增大。这一结果很好地解释了记忆合金马氏体逆转变温度As的变化规律,从而证明过饱和空位是Cu-Zn-Al形状记忆合金马氏体稳定化的一个主要原因。     

多层油纸绝缘介质不同局放阶段的陷阱参数演变规律

陷阱是影响绝缘介质中电荷输运特性的重要因素,研究不同局放阶段绝缘介质陷阱参数演变规律对深入研究放电机理具有重要意义。为此,考虑车载变压器的匝间绝缘结构特点,设计了3层油纸绝缘缺陷模型,进行局部放电试验,选取起始到击穿不同放电阶段的试样,采用等温表面电位衰减方法研究不同放电阶段3层绝缘纸试样的表面电荷衰减特性,据此计算相应阶段试样表面电荷的陷阱分布规律,探究陷阱分布对局部放电的影响。研究结果表明:Nomex纸以深陷阱为主,随着放电的发展,第1层绝缘纸陷阱能级增加,第2、3层绝缘纸陷阱能级下降;陷阱能级和陷阱密度降低导致绝缘纸中自由电荷变化率增加,自由电荷数量增多,放电的剧烈程度增加;电荷的传输特性是局部放电过程中不同层绝缘纸陷阱参数演变规律不同的重要原因。     

叶盘结构频率转向特征的量化分析研究

研究叶盘结构频率转向的曲线特征,是预测叶盘结构的失谐敏感性及振动局部化程度的重要前提。文章基于叶盘系统的集中参数模型,分析了频率转向曲线特征参数(频率曲线转向处曲率、频率曲线斜率、两频率曲线转向处最小距离)与叶盘系统结构参数(耦合刚度,轮盘质量)之间的内在联系,仿真研究了频率转向曲线特征参数随叶盘系统结构参数的变化规律。研究结果表明:频率曲线转向处曲率越大(或斜率越大),系统失谐敏感性越大;频率转向曲线间隙处附近的系统模态局部化变化剧烈,该区域模态对结构失谐非常敏感。     

中继协同无人机辅助的认知无线电网络安全通信

针对中继协同无人机（UAV）辅助的无线通信网络,提出一种基于认知无线电网络（CRN）的物理层安全通信方案。利用二次发射机协同解码转发中继向目的接收机发送机密消息,将UAV用作移动干扰器发送干扰噪声,以降低窃听者的解码能力。在不影响主用户通信的前提下,通过联合优化UAV的飞行轨迹和发射功率提高系统的平均保密率,采用基于连续凸逼近的算法求解近似凸规划的保密率最大化问题。仿真结果表明,相较于优化功率和优化轨迹这两种传统方案,该方案能够进一步提高CRN系统通信的安全性。     

贵州省气象档案管理系统建设

贵州省气象档案管理系统的研究与开发,解决了贵州省气象信息中心在日常工作中气象档案收集至归档之间的脱节问题,减少了工作量,为档案管理员提供了完整的档案业务功能。它涵盖了档案的收、管、存、用的全过程,实现对各种档案种类进行有效的组织和管理,初步建立了贵州省气象档案的自动化管理,在一定程度上提高了贵州省气象档案管理水平。     

Nested relation extraction with iterative neural network

Most existing researches on relation extraction focus on binary flat relations like BornIn relation between a Person and a Location. But a large portion of objective facts described in natural language are complex, especially in professional documents in fields such as finance and biomedicine that require precise expressions. For example, “the GDP of the United States in 2018 grew 2.9% compared with 2017” describes a growth rate relation between two other relations about the economic index, which is beyond the expressive power of binary flat relations. Thus, we propose the nested relation extraction problem and formulate it as a directed acyclic graph (DAG) structure extraction problem. Then, we propose a solution using the Iterative Neural Network which extracts relations layer by layer. The proposed solution achieves 78.98 and 97.89 F1 scores on two nested relation extraction tasks, namely semantic cause-and-effect relation extraction and formula extraction. Furthermore, we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively, which makes the annotation difficult and errorprone. Hence, we extend our model to incorporate a mentioninsensitive mode that only requires annotations of relations on entity concepts (instead of exact mentions) while preserving most of its performance. Our mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.