腔体滤波器智能调试平台设计及实现

微波滤波器应用广泛,在大批量的生产中,为了减少调试对于工程经验的依赖性,不断提高微波滤波器调试的效率,设计并实现了针对腔体滤波器智能调试平台.平台采用计算机智能提取滤波器的响应参数,通过步进电机自动调整滤波器.该调试平台在滤波器调试实际应用中获得了显著的经济效益.     

一种腔体滤波器的计算机辅助调试方法

介绍了一种对腔体滤波器进行计算机辅助调试的新方法。该方法首先建立一个理想的电路模型,运用软件DESIGNER的优化拟合功能,对滤波器的状态进行参数提取,并对滤波器各调试元件与电路参数之间的关系进行了分析。通过实际状态的参数与理想状态的参数的对比,找到二者的差异,由此可指导调试。通过对一个腔体梳状线带通滤波器进行调试,证明了该方法的有效性。     

基于支持向量回归的腔体滤波器机电耦合建模与优化

为了提高装配后的滤波器电性能和成品率,该文提出了一种基于支持向量回归的腔体滤波器机电耦合建模和优化方法。该方法根据工程中积累的经验数据,首先应用改进的多核线性规划支持向量回归算法建立了制造精度对滤波器电性能影响的机电耦合模型,然后应用此模型优化了滤波器的制造精度,从而获得了最优的机械结构尺寸。实际滤波器的实验结果验证了该方法的有效性。该方法可用于批量生产的腔体滤波器的计算机辅助制造系统中。     

同轴腔体窄带带通滤波器快速设计方法

为了满足卫星用滤波器研制周期短、可靠性高的要求,提出了一种快速设计经典同轴腔体滤波器的方法.该方法通过原型滤波器查表可以计算滤波器节数、耦合常数、群时延、单腔谐振频率、单腔Q值等初始值;通过使用三维电磁(EM)仿真软件得到单腔调谐钉长度和加载电容值的对应关系、两个腔体间耦合系数及端口抽头高度等模型SnP参数;通过电路仿真软件使用集总元件对电容加载的量值进行优化,极大地提高了仿真的速度.最后使用该协同仿真方法设计一同轴腔体滤波器,并对仿真结果进行了验证.     

用于椭圆函数滤波器高级模块化设计的单腔体计算机辅助设计

现代高级椭圆函数滤波器采用模块级联,其基本机构为单腔体(singlet),通过级联单腔体即可得到椭圆函数及准椭圆函数型直列式滤波器,大大降低了设计及调试难度。介绍了椭圆函数及准椭圆函数滤波器模块设计的基本结构——单腔体(singlet),提出了一种单腔体计算机辅助设计的方法。并用该方法设计了一个中心频率28 GHz的单腔体,其仿真结果与理论值非常一致,从而验证了该方法的有效性。     

基于多端口参数提取和主动空间映射法的腔体滤波器设计

为提高微波腔体滤波器的设计效率,文中提出了一种基于多端口参数提取和空间映射法的滤波器快速设计 方法。通过添加额外内部端口提取腔体滤波器电磁模型的Y 参数,从而计算出耦合矩阵,再结合空间映射法将求解滤 波器理想尺寸问题转换为逼近理想耦合矩阵问题。最后设计并加工了一款四阶同轴腔体滤波器,在初值偏差很大的情 况下,经过四次迭代即可得到满足指标的滤波器物理尺寸,实物调试结果和仿真结果一致性良好,从而证明了文中方法 的快捷有效。     

基片集成波导三腔感性耦合腔体滤波器的设计研究

工程实际中常用的带通滤波器设计方法是从原形低通模型出发,经过频率变换和元件参数值的变换得到集总参数谐振腔元件数值的大小,再利用微波结构加以实现。本论文以一个基片集成波导感性耦合腔体滤波器的设计为例,详细的阐述了基片集成波导腔体滤波器的设计过程。之后,按照此类腔体滤波器的设计方法,设计了多款腔体带通滤波器,包括有极高的频率选择性的交叉耦合滤波器,具有优良的性能和小型化效果。     

腔体滤波器温度补偿和温度试验

为了减小腔体滤波器随温度变化对性能的影响,利用CST仿真软件对腔体滤波器进行温度分析,并结合ADS软件对该滤波器进行模拟,得出60℃温差下的幅相变化数据和频漂数据。设计了一个采用双导体结构的滤波器,该滤波器盒体为铝合金材料,谐振杆和调谐螺钉为殷钢。通过分析温度试验中误差的影响因素,提出一种较小误差的温度试验方法,并对调试好的滤波器进行温度试验,试验结果显示该滤波器的频漂为5.6 ppm/℃,试验数据跟仿真结果吻合。     

高温度稳定性腔体滤波器研究

本文从腔体尺寸角度出发,认为某一方向的尺寸随温度是线形变化的.推导出了腔体各个部件的温漂影响因子,该因子反映了腔体的某个部件对整个腔体的温漂影响的大小.用三维仿真软件HFSS和CST去计算部件温漂影响因子,所得结果基本一致.建立了抵消温漂的模型,由该模型出发合理设计腔体各部分材料,得出无温漂的腔体.用经过温补的腔体设计出了高温度稳定性的滤波器.     

遗传神经网络在腔体滤波器结构参数优化中的应用

在工程实践中,为了减少对腔体滤波器结构参数设计的盲目性和对经验的依赖性,不断提高设计效率,提出了一种基于遗传神经网络的优化设计方法。该方法采用浮点数编码方式和自适应的交叉率、变异率,将改进的遗传算法用来优化BP神经网络的权重,在MATLAB环境下调用HFSS实现腔体滤波器的优化设计。仿真实验表明,该算法能够较精准的预测腔体滤波器的结构参数,可提高设计精度及优化速度。     

基于量子遗传的超参数自动调优算法的设计与实现

机器学习伴随着海量数据的支持以及强大的计算能力为其提供了强有力的保证下不断地向前发展,训练过程变得更加高效便捷。在此基础上,机器学习算法的超参数对其性能的影响是非常巨大的,因此对众多的超参数进行优化选择就自然有了强烈的需求。由此本文提出了一种基于量子遗传的超参数自动调优算法,实验表明,在针对多种机器学习模型的超参数调优问题上,既解决了一般随机算法的不稳定性的问题,也解决了一般进化算法迭代缓慢、收敛速度较低的问题,并且通过实验结果表明取得了不错的效果。     

Integrating machine learning with region-based active contour models in medical image segmentation

Region-based active contour models are effective in segmenting images with poorly defined boundaries but often fail when applied to images containing intensity inhomogeneity. The traditional models utilize pixel intensity and are very sensitive to parameter tuning. On the other hand, machine learning algorithms are highly effective in handling inhomogeneities but often result in noise from misclassified pixels. In addition, there is no objective function. We propose a framework which integrates machine learning with a region-based active contour model. Classification probability scores from machine learning algorithm, which are regularized using a non-linear function, are used to replace the pixel intensity values during energy minimization. In our experiments, we integrate the k-nearest neighbours and the support vector machine with the Chan-Vese method and compare the results obtained with the traditional methods of Chan-Vese and Li et al. The proposed framework gives better accuracy and less sensitive to parameter tuning.     

导频测量法--CDMA网络中传播模型校正的有效方法

校正移动通信的传播模型常用CW测试法，但因CW测试中有关测试点的选择、设备的架设等受诸多因素的影响，导致测试点数有限，同时人力、物力耗费较大。作者经过实际的测试摸索，提出了基于CDMA现网的导频测量法，大大提高了校正后传播模型的准确性，节省了人力和物力。     

单脊条形可调谐电吸收调制DFB激光器

报道了一种波长可热调谐的电吸收调制分布反馈激光器(Electroabsorptionmodulateddistributedfeedbacklaser,EML)。在激光器条形的侧面淀积一薄膜加热器,EML实现了 2 2nm的连续调谐。在调谐范围内,激光器输出功率的变化小于 3dB。采用端面有效反射率方法和耦合波理论的计算表明:采用相调制方法,可实现调谐范围达3 2nm的EML。如果热调谐与相调谐方法结合,可在较宽范围内实现波长快速调谐的EML     

一种改进的PID参数整定方法

当使用先进策略整定PID控制器参数时,往往要依赖于系统所辨识的对象模型。为了降低对系统模型的依赖性,提出了一种基于同时扰动随机逼近的在线、无模型的参数整定方法。算法的核心思想是利用高效的同时扰动技术对PID控制器性能指标函数的梯度进行估计,并利用随机逼近技术对PID的三个参数进行在线调整。仿真结果验证了该方法的有效性。     

Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization

Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been developed and successfully applied for certain application domains. However, this work demands professional knowledge and expert experience. And sometimes it has to resort to the brute-force search. Therefore, if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method, it will greatly improve the efficiency of machine learning. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes. In this way, the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem. Bayesian optimization is based on the Bayesian theorem. It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function. A utility function selects the next sample point to maximize the optimization function. Several experiments were conducted on standard test datasets. Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models, such as the random forest algorithm and the neural networks, even multi-grained cascade forest under the consideration of time cost.     

A torque estimator using online tuning grey fuzzy PID for applications to torque-sensorless control of DC motors

DC motors are used indispensably in industrial applications because they provide such advantages as small size, high speed, low construction cost, and safe operation. A major area of research in DC motors is to determine a better method to measure the torque of motor shaft. The traditional way to measure the mechanical torque of a rotating shaft is attachment a torque transducer in the transmission system between the driving motor and the load. However, this technique requires additional parts for the transmission system, which makes the design more complicated, time consuming, costly in construction, and in many cases impossible to achieve.The purpose of this paper is to present a new method for estimating the load torque of a DC motor shaft by using a novel modelling method based on an adaptive control technique, named as online tuning grey fuzzy PID (OTGFPID). A test rig using a DC motor is setup to investigate the torque behaviour as well as to evaluate the developed estimator. Firstly, mathematical model is developed for the motor. Secondly, the experimental speed-torque data and the optimized motor model is used to optimize the torque estimator. Then the optimized estimator is used to estimate accurately the load torque. Finally, the capability of the optimized torque estimator has been validated with the practical experiments in comparison with a typical estimation method.     

磁共振成像仪的线圈调谐检测模块设计

线圈作为磁共振成像系统的重要组成部分,其性能直接影响成像质量.为了尽可能保证信噪比和图像质量,探测前需进行线圈调谐.调谐时,通过微调线圈的共振频率和阻抗两个参数,使线圈共振频率与拉莫尔频率一致,线圈和前放电路阻抗匹配.本文设计了一种线圈调谐的检测模块,阐述了模块的工作原理和软硬件设计.模块包括测量、处理两个部分,其中采用STM32采集电压数据,通过数据转换后用液晶屏显示实时波形.实际结果表明,此检测模块不仅延续了上位机调谐显示直观清晰的优点,而且兼具独立数码显示表的操作便捷的优点.     

Intelligent alignment of waveguide filters using a machine learningapproach

The authors investigate the application of a machine learning system to the tuning of waveguide filters. This system uses techniques from pattern recognition and adaptive signal processing. The manual tuning of the waveguide filters is very time consuming and expensive and a skilled operator is required. Here, the machine learning system is adapted in such a way that it can assist an unskilled operator to perform fast and accurate tuning of these filters. The machine learning approach is based on the manipulation of some raw data to extract a set of salient features that have strong significance in the behavior of the filters. These features are derived visually, by comparing the characteristics of a tuned filter to those of a faulty filter with known levels of maladjustments