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1.
高端芯片制造所需要的极紫外光刻技术位于我国当前面临35项"卡脖子"关键核心技术之首.高转换效率的极紫外光源是极紫外光刻系统的重要组成部分.本文通过采用双激光脉冲打靶技术实现较强的6.7 nm极紫外光输出.首先,理论计算Gd18+—Gd27+离子最外层4d壳层的4p-4d和4d-4f能级之间跃迁、以及Gd14+—Gd17+离子最外层4f壳层的4d-4f能级之间跃迁对波长为6.7 nm附近极紫外光的贡献.其后开展实验研究,结果表明,随着双脉冲之间延时的逐渐增加,波长为6.7 nm附近的极紫外光辐射强度呈现先减弱、后增加、之后再减弱的变化趋势,在双脉冲延时为100 ns处产生的极紫外光辐射最强.并且,在延时为100 ns处产生的光谱效率最高,相比于单脉冲激光产生的光谱效率提升了33%.此外,发现双激光脉冲打靶技术可以有效地减弱等离子体的自吸收效应,获得的6.7 nm附近极紫外光谱宽度均小于单激光脉冲打靶的情形,且在脉冲延时为30 ns时刻所产生的光谱宽度最窄,约为单独主脉冲产生极紫外光谱宽度的1/3.同时...  相似文献   
2.
The observation and study of nonlinear dynamical systems has been gaining popularity over years in different fields. The intrinsic complexity of their dynamics defies many existing tools based on individual orbits, while the Koopman operator governs evolution of functions defined in phase space and is thus focused on ensembles of orbits, which provides an alternative approach to investigate global features of system dynamics prescribed by spectral properties of the operator. However, it is difficult to identify and represent the most relevant eigenfunctions in practice. Here, combined with the Koopman analysis, a neural network is designed to achieve the reconstruction and evolution of complex dynamical systems. By invoking the error minimization, a fundamental set of Koopman eigenfunctions are derived, which may reproduce the input dynamics through a nonlinear transformation provided by the neural network. The corresponding eigenvalues are also directly extracted by the specific evolutionary structure built in.  相似文献   
3.
法浩  刘翠 《化学教育》2022,43(7):15-20
学习体验是学生对学习内容、过程、方法、意义的自我感受和评估,对学生学习志趣、学习素养提升有积极正向作用。以钠及其化合物复习为例,将各类别物质间转化关系的构建、物质制备原理和条件的优选作为学习体验对象,依据学习体验的内在发展历程设计学习任务及活动,引导学生亲身体验学科认知方式、学科思维方法、学科应用价值,评价体验结果,激励学生自主学习发生。  相似文献   
4.

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

  相似文献   
5.
为克服机器学习方法在油藏单井产量预测中的过拟合问题, 提高油田生产中的产量预测精度, 提出一种基于条件生成式对抗网络(CGAN)的油藏单井产量预测模型。该模型使用长短期记忆、全连接等基础神经网络, 构建生成和判别网络模型。生成网络模型以产量影响因素为条件输入, 生成预测产量数据, 利用对数损失函数评价预测数据与真实数据之间的偏差, 通过条件生成式对抗网络的博弈训练, 并结合贝叶斯超参数优化算法, 优化模型结构, 综合提高模型的泛化能力。基于Eclipse数值模拟软件建立同一井网条件下不同地质和生产条件下的油藏单井产量数据库, 以地质与生产条件等产量影响因素作为模型的条件输入, 进行油藏单井产量预测。结果表明: 与全连接神经网络(FCNN)、随机森林(RF)以及长短期记忆神经网络(LSTM)模型的预测结果相比, CGAN模型在测试集上的平均绝对百分比误差分别提升了2.59%、0.81%以及1.72%, 并且过拟合比最小(1.027)。说明CGAN降低了机器学习产量预测模型的过拟合程度, 提高了模型的泛化能力与预测精度, 验证了所提算法的优越性, 对指导油田高效开发和保障我国能源战略安全具有重要意义。  相似文献   
6.
深度学习在检测领域高速发展,但受限于训练数据和计算效率,在基于嵌入式平台的边缘计算领域,尤其是实时跟踪应用中深度学习的智能化算法应用并不广泛。针对这一现象,同时为满足现阶段国产化、智能化的技术需求,提出了一种改进的孪生网络深度学习跟踪算法。在特征网络加入微调网络,解决了网络模型无法在线更新的问题,提升了跟踪的准确性;在IoUNet损失函数中加入中心距离惩罚项,解决了IoUNet当IoU相同时位置跳跃,存在收敛盲区和收敛速度慢的问题;将训练后的网络通过通道剪枝,缩减网络模型尺寸,提升了模型加载和运行的速度。在华为Atlas200NPU平台上实现了实时运行,算法准确率高达0.90(IoU>0.7),帧率达到66 Hz。  相似文献   
7.
To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electron-laser and plasma wakefield acceleration, a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems. Due to their intrinsic one-to-many property, current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution. Here we propose a real-time solver for one-to-many problems of temporal shaping, with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN). We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles. This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields.  相似文献   
8.
We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.  相似文献   
9.
Indirect learning architecture (ILA) for digital pre-distortion (DPD) is commonly used to linearize power amplifiers (PA). To the author’s best knowledge, most of the DPD results in the literature obtain the matrix form of the least-square solution in order to get the DPD coefficients numerically. There exists no explicit closed-form for these coefficients that can be used as plug-and-play in simulations, or used for further closed-form analysis of important measures such as signal-to-noise ratio (SNR) and mean square error (MSE), bit-error rate (BER), …etc. In this paper, we analyze the ILA-DPD system for general memory-polynomial PA models. We provide a closed-form solution for the DPD coefficients. We first present the analytical methodology for deriving the mathematical expressions for each DPD coefficient and then introduce an open-access code that generates the DPD coefficients in symbolic form that is used to mathematically model the DPD. We consider case studies for PA and show that the analytical DPD solution matches the Monte Carlo simulations. Moreover, we also provide a closed-form solution for the iterative adaptive ILA-DPD. Our analysis shows that in the case of a large training block length the non-iterative DPD achieves approximately the same performance as an iterative DPD with a shorter training block length. System impairments are also considered, e.g. the thermal noise and the quantization noise in analog–digital conversion (ADC). We derive the normalized mean square error (NMSE) for the transmit chain in the presence of these impairments. The NMSE expression is verified through numerical simulations.  相似文献   
10.
宋扬 《化学教育》2022,43(16):76-81
实践创新素养是中学生核心素养的重要组成部分。以选择性必修模块3中“合成高分子”内容为例,以“棉粮争地”问题为背景,以项目式研究的形式在化学课堂上进行实践创新素养的教学尝试,并取得较好的结果。同时还引入了班内分层教学的尝试,为因材施教进行了有效的尝试。  相似文献   
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