铀矿井下作业人员职业性放射性肺癌病例诊断评价

本文通过对8例铀矿井下作业工人肺癌病因判断的回顾性分析,进一步了解高氡暴露下职业人员患肺癌的高危性和职业性放射性肺癌病因概率判断的科学性,有助于加强铀矿井下工作人员的辐射防护和肺癌病因判断的分析研究。     

装配整体式网格剪力墙压弯承载力计算方法

通过6榀装配整体式网格剪力墙试件的低周反复荷载试验,研究了预制墙板竖向钢筋连接方式、竖向接缝形式,布筋方式对墙体抗震性能及压弯承载力的影响,试验结果表明：6榀墙体均发生以竖向边缘构架破坏为主的弯曲型破坏;预制墙板竖向钢筋采用预埋件焊接时,能有效提升墙体的承载力;预制墙板竖向接缝形式对墙体的承载力影响不大;井字形布筋墙体的承载力及延性略大于其他类型布筋墙体。基于平截面假定,充分考虑预制墙板可靠连接钢筋作用,忽略未连接钢筋作用,建立墙体在开裂、屈服、峰值、极限状态弯矩-曲率计算方法,并对影响其各阶段承载力计算公式的因素进行分析。结果表明,所建立的弯矩-曲率计算公式能较为准确地描述装配整体式网格剪力墙各阶段的荷载-变形关系,计算值与试验结果吻合较好。     

基于随机博弈与禁忌搜索的网络防御策略选取

网络防御策略是决定网络安全防护效果的关键因素,现有的网络防御决策研究的是完全理性前提条件以及攻防效益函数参数选择等方面,对实际网络攻防中信息不对称、法律惩戒等因素存在模型偏差,降低了策略的实用性与可靠性.结合实际问题,在有限理性的前置条件基础上构建禁忌随机博弈模型,引入了禁忌搜索方法对随机博弈进行有限理性的分析,并设计具有记忆功能的搜索方法,通过禁忌表数据结构实现记忆功能,并利用数据驱动的记忆结合博弈模型得出最优防御策略.实验结果表明：该方法在攻防收益量化方面提高了精准度,防御效益相对于现有典型的方法提高了准确度,方法空间复杂度优于强化学习等典型方法.     

一种新型调频广播授时信号体制研究

利用调频广播附加信道作为载波进行时间信息的传递为调频广播授时奠定了基础,该文基于对调频广播附加信道的研究,提出新的授时扩频码设计方法并详细设计了扩频码内容,既符合信道条件要求,又有利于接收端的精确捕获跟踪,从而进行时间信息的传递并实现授时功能,实测结果验证了该方法的可行性和准确性。

     

GPS III首星信号结构及其特性分析

导航信号是连接空间卫星和地面用户端的枢纽,是卫星导航定位系统中最重要的部分之一,其优劣直接影响后续的定位、测速、授时等性能。该文利用国家授时中心40 m高增益天线对GPS III首星进行多次信号采集及比对分析工作,从GPS III L1频点调制矢量及频谱分布入手,对L1频点载波相位存在“滑动”现象进行了深入剖析,得出该现象主要源于L1M信号。利用C/A码解析出授权信号M码,定量分析了L1频点信号分量的S曲线过0点偏差以及功率占比,其中L1M S曲线过0点偏差达到0.058 ns,信号功率占比最高可达6.78。该文研究成果可为研究新一代GPS信号的调制方式提供支撑,也可为后续北斗导航卫星系统信号体制设计以及信号质量评估方法提供参考。     

基于TDC的无死区频率测量技术研究

在精密时频测控领域中,高分辨率、无死区的时间间隔和频率测量非常关键,而时间数字转换器(Time to Digital Converter, TDC)是时间频率测量的常用手段。该文研制了基于ACAM公司生产的时间数字转换芯片TDC-GP21和Altera公司FPGA芯片EP4CE6E22C8N的时间频率测量设备,实现了高分辨率的时间间隔测量,测量分辨率达到13ps 。同时采用时间间隔测量模块两两组合的方式实现了无死区频率测量,创新性地采用每组3个TDC芯片,共4组搭建了时间频率测量系统,并对组内3个TDC芯片测量结果采用平均值滤波法,使频率测量稳定度达到$ 1.1 $×$ {10}^{-11} $@1 s, $ 5.6\times {10}^{-15} $@10000 s,与商用K+K FXE频率计数器指标相当。本设备具有体积小、无需校准、成本低等优点,能够广泛应用到高精度时间间隔和精密频率测量领域中。     

Continuously Flow Photothermal Catalysis Efficiently CO2 Reduction Over S-Scheme 2D/0D Bi5O7I-OVs/Cd0.5Zn0.5S Heterojunction with Strong Interfacial Electric Field

Using CO₂, water, and sunlight to produce solar fuel is a very attractive process, which can synchronously reduce carbon and convert solar energy into hydrocarbons. However, photocatalytic CO₂ reduction is often limited by the low selectivity of reduction products and poor photocatalytic activity. In this study, S-scheme Bi₅O₇I-OVs/Cd_0.5Zn_0.5S (Bi₅O₇I-OVs/CZS-0.5) heterojunction with strong interfacial electric field (IEF) is prepared by in situ growth method. The performance of reduction CO₂ to CO is studied by continuous flow photothermal catalytic (PTC) CO₂ reduction platform. 12.5% Bi₅O₇I-OVs/CZS-0.5 shows excellent CO yield of 58.6 µmol g⁻¹ h⁻¹ and selectivity of 98.4%, which are 35.1 times than that of CZS-0.5 under visible light. The charge transfer path of the S-scheme through theoretical calculation (DFT), in situ irradiation Kelvin probe force microscope (ISI-KPFM) and in situ irradiation X-ray photoelectron spectroscopy (ISI-XPS) analysis, is verified. The study can provide useful guidance and reference for improving activity by oxygen vacancy induced strong IEF and the development of a continuous flow PTC CO₂ reduction system.     

基于线激光扫描的零件三维表面检测系统研究

零件表面缺陷是造成零件生产损失的主要原因之一,现如今的表面缺陷检测大多集中于平面的表面缺陷检测,为了能更好地表达物体的三维表面信息以及针对其三维信息进行缺陷检测,提出了一种基于线激光扫描的零件三维表面检测系统.首先对相机进行标定,可用于对相机采集到的图像进行矫正,同时得到相机的内外参数,用于后续图像处理的坐标转换.然后...     

Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

Session-based Recommendation (SBR) aims to accurately recommend a list of items to users based on anonymous historical session sequences. Existing methods for SBR suffer from several limitations: SBR based on Graph Neural Network often has information loss when constructing session graphs; Inadequate consideration is given to influencing factors, such as item price, and users’ dynamic interest evolution is not taken into account. A new session recommendation model called Price-aware Session-based Recommendation (PASBR) is proposed to address these limitations. PASBR constructs session graphs by information lossless approaches to fully encode the original session information, then introduces item price as a new factor and models users’ price tolerance for various items to influence users’ preferences. In addition, PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time. Finally, PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction. Specifically, the intent, the short-term and long-term interests, and the dynamic interests of a user are combined. Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.