FDTD 结合支持向量机反演各向异性材料电磁参数

本文首先介绍了太赫兹波导和3D打印技术的发展现状。3D打印作为一项新兴的技术,以数字模型文件为基础,运用粉末状金属或塑料等可粘合材料通过逐层打印的方法构造实体,打破了传统THz波导技术的局限性。本文介绍的3D打印THz波导利用聚合树脂作为打印材料,打印完成的THz波导在其传输通路上镀500nm的金,金的厚度足以支持THz传播。利用这种方法可以打印出直波导、三维弯曲面、三维Y劈和U型波导等多种结构。3D打印THz波导除传输损耗略高外,其传输模式及其特性与传统的金属波导基本一致,这种额外的传输损耗归咎于商业3D打印机的精度。     

改进的kNN分类算法在工业物联网中的应用

在工业物联网中,k近邻分类(kNN)被广泛应用于缺陷产品识别和异常检测.但kNN自身存在计算复杂度高、不适用于分布式环境等缺点.因此,文中提出了一种安全有效的分布式kNN分类算法,以防止信息泄漏和控制流泄漏,同时支持分布式服务器上的大规模数据分类.首先设计了一个安全有效的向量同态加密方案.在该方案的基础上,提出了DkN...     

基于深度学习的SAR图像舰船目标检测综述

近年来,合成孔径雷达成像技术因具备全天时和全天候的目标感测能力,在海洋实时监测和管控等领域发挥着重要作用,特别是高分率SAR图像中的舰船目标检测成为当前的研究热点之一.首先分析基于深度学习的SAR图像舰船目标检测流程,并对样本训练数据集的构建、目标特征的提取和目标框选的设计等关键步骤进行归纳总结.然后对检测流程中的各部...     

Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory’s viability and performance were implemented using IBM’s Qiskit library.     

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users’ local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps—which run calculations “in the cloud” rather than on users’ local computers—browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.     

锂离子电容器卷芯外形视觉在线检测系统设计

为解决锂离子电容器卷芯的生产设备钉卷机检测能力不足、操作者劳动强度大、效率低等问题,设计了一款基于机器视觉的锂离子电容器卷芯外形尺寸在线检测系统.应用高清CMOS工业相机、PLC、光电传感器等搭建硬件平台并安装到钉卷机上,设计了适用于锂离子电容器卷芯检测的图像处理算法,并基于此对该系统进行验证.结果表明,该系统具有识别...     

基于SMO-SVM的丝杠表面磨削质量预测

针对滚珠丝杠磨削过程在线监测困难的问题,使用支持向量机建立智能模型。模型本身针对颤振分类和粗糙度预测的不同问题,选用基于串行优化算法的支持向量分类机和支持向量回归机,并使用交叉验证法对模型参数进行优化。基于滚珠丝杠表面波纹度理论和粗糙度理论,对磨削过程中的振动信号进行特征提取,结合加工参数作为模型输入,先进行颤振的判别,在判断未颤振的情况下对表面质量进行预测。实验结果表明,该模型可以对颤振分类及粗糙度预测进行较好的在线监测。