S Zorb装置反应器接收器脱气线和再生器取热盘管改造

某公司对S Zorb装置首个运行周期内出现的反应器接收器脱气线泄漏、再生器取热盘管泄漏等运行问题开展了技术攻关,提出了工艺优化调整、工程技术改造建议。2019年大检修时对反应器脱气线进行扩径,由DN100扩径为DN150,控制气提量小于670 m^(3)/h,对再生系统取热蒸汽和再生风流控阀等进行了工程技术改造。第二运行周期未出现脱气线减薄泄漏和再生器取热盘管泄漏问题。2023年6月大检修时对上次大检修改造部位进行检查以验证工程改造的有效性,经全面检查,确认脱气线内部未发现冲刷痕迹,再生器取热盘管打压合格,外观正常,证明改造措施有效,可以满足“四年一修”目标要求。

一种超宽共模输入范围的CAN FD接收器

针对在负压干扰条件下,控制器局域网(Control Area Network,CAN)总线接收器存在工作异常以及传输延时较大的问题,设计了一种超宽共模输入范围的可变速率的CAN(CAN with Flexible Data rate,CAN FD)接收器。该设计通过电平移位与共射级相结合的方式,以减少总线信号对输入级静态工作点影响,拓展接收器的负压输入范围;采用电阻分压的方式衰减总线信号电平,以减轻输入级差分对管的耐压负担;引入低温漂电压,以产生高精度的翻转阈值;在输出级模块增添限幅晶体管,以限制接收器输出摆幅,提升传输速度。芯片采用0.18μm 60 V双极、互补、双扩散金属氧化物半导体工艺进行电路设计。仿真结果表明,在5 V电源电压条件下,设计接收器的共模输入范围为-60~60 V,阈值电压为0.71 V,迟滞门限为120 mV,温漂为0.057 mV/℃,平均传输延时小于55 ns。与相关设计相比,设计的接收器具有较宽的共模输入范围,传输延时较小,符合CAN协议规范。

基于多路蓝牙接收器的无人机跟踪系统

随着无人机日益受到人们关注,对无人机交通的管理与控制变得尤为重要,尤其需要开发用于监控无人驾驶飞行器的系统,以实现对无人机的监控和追踪。文章提出了一种基于多路蓝牙低功耗接收器的无人机位置跟踪系统。该系统首先通过距离和角度的估算确定目标位置,同时在4个蓝牙接收器上使用接收到的信号质量指示(RSSI)信号。基于估算的距离和角度,系统逐渐监控目标位置。为提高估算算法的准确性,文章引入了一种新的记忆方法,对运动数据进行重新提取。

空间光通信接收器孔径平均函数的二维映射计算方法

针对现有研究中缺乏计算具有任意形状光学接收器孔径平均函数方法的问题,提出了一种新颖的二维映射方法。首先,将接收器的数学形状映射为一个二维矩阵,并基于该矩阵构建基准孔径、位移孔径,并确定位移范围。然后,通过逐步移动位移孔径获得一系列可变的位移矩阵。随后,通过计算位移矩阵和基准矩阵之间的相同元素,得到不同质心间距矢量下的孔径重叠面积,从而精确地求得接收器孔径平均函数。最后,以计算卡塞格林望远镜的孔径平均函数为例,将其映射为300×300的矩阵,并与理论计算结果进行了比较。对比结果表明,该方法在计算孔径平均函数时具有极小的误差,验证了所提方法的准确性和可靠性。

应用于脉冲ToF激光雷达接收器的宽带高增益跨阻放大器

设计了一款应用于脉冲飞行时间(ToF)激光雷达(LiDAR)接收器的宽带高增益跨阻放大器(Transimpedance Amplifier,TIA)。该TIA由跨阻预放大器、后级电压放大器、增益补偿放大器和输出缓冲器组成。跨阻预放大器采用改进型RGC电路实现;后级电压放大器采用有源反馈电路,以提升带宽;具有有源峰值电感的增益补偿放大电路用来消除大尺寸输出缓冲器对TIA整体带宽的影响。提出的TIA电路采用0.18μm标准CMOS工艺设计。在电源电压为3.3 V条件下,TIA总消耗功率约168 mW,差模跨阻增益为106 dBΩ,-3 dB带宽约为300 MHz,输入参考噪声电流谱密度为6.8 pA/√Hz,当SNR=5时,能够检测的最小光电流约为0.7μA,动态范围为83 dB。

集成方位跟踪太阳能塔式接收器热流密度分布特性研究

为研究接收器与竖直方向夹角和射线集中点的位置对热流密度值与分布的影响,我们以集成方位跟踪太阳能集热实验装置中集热单元为研究对象,基于SolTrace软件和射线追踪技术搭建了定日镜和接收器光学模型。以光学模型为基础,通过改变接收器的角度和位置来改变热流密度值与分布,得到接收器在不同位置时的热流密度值与分布状态。结果表明:接收器上热流密度值在接收器与竖直方向夹角为0°~90°时,随夹角的增大先增后减,夹角为40°时其值达到最大;射线焦点在接收器开口焦点2位置附近时热流密度分布较为均匀,射线可全部进入接收器内,热流密度峰值只有70 kW/m^(2)左右。建模仿真结果对集热单元中接收器安装和位置的调节提供了有益参考。

S Zorb装置反应器接收器底部法兰泄漏原因分析及对策

为解决延安石油化工厂180万t/a S Zorb装置反应器接收器底部法兰多次泄漏的问题,拆解磨损泄漏部位法兰、通气环等,从通气环通入的循环氢流量、通气环上下法兰受力情况及螺栓预紧力等方面入手分析,得出主要原因为通气环氢气流量控制不精确、螺栓预紧力下降。通过增加转子流量计、增配碟簧、合理设置支撑等措施,有效解决了反应器接收器底部法兰易泄漏的问题。

蓝牙接收器的超值之选——东方之声BT-1(25周年纪念版) 蓝牙接收器、LPS-1线性电源

随着蓝牙技术的发展以及网络音乐平台逐步加入高格式音乐服务,早几年发烧友们还是之以鼻的通过蓝牙播放音乐,现在也被发烧友们所接受。同时也有越来越多的传统音响厂商推出单独的蓝牙接收器,又或者在功放、解码等器材内加入蓝牙功能,通过手机/平板的蓝牙播放音乐也逐渐成为了很多爱乐者听音乐的方式。这次我们收到的是一款国产老牌子的蓝牙接收器——东方之声BT-1(25周年纪念版)以及搭配的LPS-1线性电源。

带窗口比较器的高速RS-485接收器

将窗口比较器和高速比较器相结合,在实现RS-485接收器开路、短路和端接失效保护功能的前提下,解决了阈值偏斜方法造成的工作速率低的问题。采用5 V工艺,在稍增接收器静态功耗(5%)和版图面积(10%)后,将工作速率提高到50 Mbit/s。

Adapter Based on Pre-Trained Language Models for Classification of Medical Text

We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.

一种可见光通信光接收器的优化与实验

可见光通信在强背景光应用场景中面临着严重的干扰问题。针对自然光干扰,基于多级阻挡原理,提出一种光接收器优化方案,并在强自然光环境中对正弦波信号和字符分别进行了传输实验。实验结果表明,该方案能够有效抑制自然光,显著改善通信性能,为强背景光场景中的可见光通信提供了一种有效的光接收器。

一些基本形状光学接收器的孔径平均函数的特性的研究

研究了一些基本形状的光学接收器的孔径平均函数的特性。历史上所使用的大气光学接收器的数学形式仅限于有限的几种,包括圆形、环形和相应阵列。学术界并未建立对光学接收器的孔径平均函数的特性的基础性和归纳性的认识。本文计算了其它一些基本形状的光学接收器的孔径平均函数,比如正方形、三角形、一维的方形阵列、二维的方形阵列。本文对其特性进行了总结。

一种宽共模输入范围的高速CAN总线接收器设计

为保证24 V供电的汽车电子系统在复杂环境下可靠通信,同时满足CAN FD总线协议高速率的要求,设计了一种宽共模输入范围的高速CAN总线接收器。采用三级放大结构实现,输入级预放大电路通过共模位移与共射极相结合的方法,扩大共模电压输入范围;增益级为输出限幅的折叠式差分放大结构,提升接收器的速度和增益;输出级利用反相器放大级,实现模拟信号到数字信号的高速转换。基于0.18μm 60 V BCD工艺完成设计与流片,测试结果表明,5 V电源电压下,接收器阈值电压的温漂系数在-40~150℃范围内为75×10^(-6)/℃,共模电压输入范围为±30 V,传输延迟小于58 ns。

基于接收信号强度预测的自适应垂直切换算法

城市部署的超密集异构无线网络中车辆终端的运动状态和业务类型差异巨大,针对不同运动状态和业务类型的终端并发接入导致的频繁切换和拥塞问题,提出一种基于接收信号强度预测的自适应垂直切换算法。在切换触发阶段使用长短时记忆神经网络预测接收信号强度,在信道链路质量恶化之前提前触发切换,在网络选择阶段综合考虑网络参数以及终端在不同运动状态和业务类型下的接入偏好,并选出综合效益值最高的网络作为切换目标网络。仿真结果表明,该算法能更好地适应终端运动状态和业务类型的变化,能降低不必要的切换次数和网络拥塞度。

一种双天线GNSS接收机混合干扰抑制方法

相对于单天线GNSS接收机,阵列GNSS接收机具有空间分辨能力,当干扰信号与卫星信号在时域频域上产生混叠时,其能够从空域上对干扰信号进行抑制,具有更强的干扰抑制能力。但阵列GNSS接收机相对于单天线GNSS接收机需要更多的阵元,随着阵元数目的增加,系统成本也相应的增加,限制了阵列GNSS接收机的应用范围。双天线GNSS接收机既具有空域抗干扰能力,同时又具有价格低廉的特点,是一种较好的折中选择。对于单一的连续波干扰,其能够产生很好的抑制效果,但是当连续波干扰中混有脉冲干扰时,由于受到自由度的限制,双天线GNSS接收机无法对混合干扰进行有效抑制,进而影响接收机的正常工作。针对于上述问题,本文提出一种新的混合干扰抑制算法。首先利用脉冲的时域特征,对待处理信号进行分块处理,确保至少有一个数据块中不含有脉冲干扰,随后对不含脉冲干扰的数据块,使用空时最小功率(Space-Time Adaptive Processing Power Inversion,STAP-PI)算法得到最优权值,然后利用该权值抑制原信号中的连续波干扰。最后,对处理之后信号中残余的脉冲干扰进行时域置零处理,从而达到抑制混合干扰的目的。仿真实验和实采实验结果均证明了所提算法的有效性。

槽式太阳能倒梯形腔体接收器热性能实验研究及㶲分析

针对适用于槽式太阳能系统的倒梯形腔体接收器,通过实验测试了不同因素对系统热性能的影响,并基于热损失率、㶲效率及㶲系数等多指标进行综合评价。研究结果表明:在研究范围内,流量、传热工质温度、环境风速对热性能的影响较大。热损失率和㶲效率均随流量的增大而减小。工质入口温度达到363K时,热损失率达到最大值为57.11%;工质入口温度达到355.7K时,㶲效率达到最大值为8.43%;风速由0.8m/s增大至2.6m/s,热损失率增加了70.1%,㶲效率减小了16.5%。㶲系数受工质入口温度、流量的变化影响较为显著。

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.