HFETR热功率测量系统误差分析

本文针对HFETR热功率自动测量系统,详细介绍了该系统的测量方式、组成和原理,在某炉段满功率运行和连续冷却两种工况下对相应的热功率计算误差进行分析,得到最大相对误差值为0.59%,一个运行周期的积分功率最大相对误差值为0.66%。本文分析了测量仪表精度对热功率计算的影响。研究表明,HFETR的热功率测量系统精度符合设计要求,满足反应堆安全运行的需要。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

广义正交码索引调制系统的性能分析

摘 要：为了提高码索引调制（code index modulation，CIM）系统的传输效率，提出了一种具有更低复杂度的单输入单输出（single input single output，SISO）的广义正交码索引调制（generalized orthogonal code index modulation，GQCIM）系统。CIM 系统使用扩频码和星座符号传输信息，但只能激活两个扩频码索引和一个调制符号。而 GQCIM 系统以一种新颖的方式克服了只激活一个调制符号的限制，同时充分利用了调制符号的正交性，增加扩频码索引以传输更多的额外信息位，提高了系统的传输效率。此外，分析了GQCIM系统的理论性能，推导了误码率性能的上界。通过蒙特卡罗仿真验证了GQCIM系统的性能，对比发现GQCIM系统的理论和仿真性能一致。而且在相同的传输效率下，结果显示GQCIM系统的性能优于同样具有正交性的调制系统，如广义码索引调制（generalized code index modulation，GCIM）系统、CIM系统、码索引调制-正交空间调制（code index modulation aided quadrature spatial modulation，CIM-QSM）系统、码索引调制-正交空间调制（code index modulation aided spatial modulation，CIM-SM）系统、脉冲索引调制（pulse index modulation，PIM）系统。     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

Context-free grammars to detect straight segments and a novel polygonal approximation method

A new method for the polygonal approximation is presented. The method is based on the search for break points through a context-free grammar, that accepts digital straight segments with loss of information, as well as the decrease in the error committed employing the comparison of a tolerable error. We present an application of our method to different sets of objects widely used, as well as a comparison of our results with the best results reported in the literature, proving that our method achieves better values of error criteria. Besides, a new way to find polygonal approximations, with context-free grammars to recognize digital straight segments without loss of pixels, it is also addressed.     

原油含水在线检测影响因素的研究

微波法作为原油开采过程中最常用的含水率检测方式,其检测效果极易受到实际开采环境中多因素影响从而产生检测误测。试验分析了矿化度以及温度两种实际开采环境下存在的因素对于微波实时检测含水率精度的影响。通过对试验数据的处理,运用二次拟合有效得出含水检测值伴随矿化度(矿化度组分:CaCl_2)的变化规律。利用试验所得规律,可有效将微波含水传感器精度提高,为提高微波含水检测精度的研究提供了校正参数和理论依据。     

基于激光测距的机载光电成像系统目标定位

针对小型机载光电平台无法准确获取视轴指向问题，设计了一种基于激光测距的目标定位算法。利用机载光电侦察平台锁定跟踪目标的特性，对同一目标多次测量，采用激光测距装置获取目标与载机间的距离信息。根据WGS-84定义的地球椭球模型建立系统的测量方程。考虑到测量方程的非线性，利用扩展卡尔曼滤波对目标位置进行估计。该定位方法精度只受到GPS接收机定位精度和激光测距机测量精度的影响，目标定位误差与机载光电侦察设备视轴指向测量无关。采用蒙特卡洛法仿真分析载机位置测量误差及激光测距系统位置误差对目标定位的影响，结果显示该算法定位精度较高。采用飞行试验数据验证了该目标定位算法的有效性，在飞行高度8 000 m时，目标定位精度优于8 m。相比于传统定位算法，该方法可将定位精度明显提高。同时此定位方法易于部署，可操作性强，具有较大的应用价值。     

基于电信号特征分析的电机故障诊断技术

介绍了电机故障诊断技术平台的组成,分析了电机常见故障下电信号的特征。利用宜昌和景洪工厂两台电机进行了实际对比测试,宜昌工厂电机定子电流特征频率在49.2Hz时表现出峰值-36.08dB,在50.8Hz时表现峰值则为-37.23dB,表明电机存在转子断条故障;景洪工厂电机在转子特征频率为113.51Hz时,出现峰值-58.47dB,表明电机存在静态偏心故障。经电机现场抽芯检查,验证了电机故障快速诊断平台给出的电机健康状态评估结果,证实了基于电信号特征分析的电机故障诊断技术的准确性。     

双螺旋结构电容式棒位测量传感器偏心性能研究

控制棒棒位测量传感器是低温核供热堆控制棒步进和落棒测量的关键设备，其精度和可靠性直接关系到反应堆的安全性。对于双螺旋结构电容式棒位测量传感器，完成了传感器静态特性实验，利用有限元分析方法建立了传感器的计算模型，模型计算结果与实验结果符合良好。在此基础上，采用有限元模型结合正交试验方法分析了极板张角、极板螺距、陶瓷管壁厚度等设计参数对传感器偏心性能的影响，获得了传感器设计参数的优化组合。研究结果表明，减小极板张角、增大极板螺距、增大陶瓷管壁厚度、减小陶瓷管相对介电常数有助于减小传感器偏心误差，由正交试验方法获得的参数组合能实现棒位测量不失步的要求。研究成果为双螺旋结构电容式棒位测量传感器的设计和优化奠定了基础。