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1.
Dispersive liquid-liquid microextraction is one of the most widely used microextraction techniques currently in the analytical chemistry field, mainly due to its simplicity and rapidity. The operational mode of this approach has been constantly changing since its introduction, adapting to new trends and applications. Most of these changes are related to the nature of the solvent employed for the microextraction. From the classical halogenated solvents (e.g., chloroform or dichloromethane), different alternatives have been proposed in order to obtain safer and non-pollutants microextraction applications. In this sense, low-density solvents, such as alkanols, switchable hydrophobicity solvents, and ionic liquids were the first and most popular replacements for halogenated solvents, which provided similar or better results than these classical dispersive liquid-liquid microextraction solvents. However, despite the good performances obtained with low-density solvents and ionic liquids, researchers have continued investigating in order to obtain even greener solvents for dispersive liquid-liquid microextraction. For that reason, in this review, the evolution over the last five years of the three types of solvents already mentioned and two of the most promising solvent alternatives (i.e., deep eutectic solvents and supramolecular solvents), have been studied in detail with the purpose of discussing which one provides the greenest alternative.  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.
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   
4.
该文提出了一种基于麻雀搜索算法结合深度前馈神经网络(SSA-DFN)的近红外光谱模型转移方法。使用深度前馈神经网络拟合不同仪器采集到的光谱之间的非线性函数映射,并将麻雀搜索算法用于网络各层连接权值和阈值的初始化,通过种群中个体位置的迭代更新,求得连接权值和阈值的最优初始值;通过多次调整深度前馈神经网络模型的超参数,使网络拟合效果趋于最优,最终确定转移函数。为验证方法的有效性,分别从烟叶近红外光谱谱图、主成分投影和预测结果的角度,将SSA-DFN方法与分段直接校正算法(PDS)、典型相关性分析算法(CCA)转移前后的效果进行了对比。结果表明SSA-DFN方法转移后的从机光谱与原主机光谱重合度最高,转移后主、从机总糖、烟碱含量的预测结果差异不显著,预测平均误差从8.32%、9.15%分别降至4.65%、4.82%,预测均方根误差(RMSEP)和决定系数(R2)等指标均优于PDS和CCA,取得了最佳的转移效果,可满足企业需求。结果表明该方法是一种有效的模型转移方法。  相似文献   
5.
With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.  相似文献   
6.
超导量子干涉仪、 超导光子探测器等深空探测器需要液氦温区制冷技术提供极低温温度, 固体界面接触热阻的存在会增大耦合界面温度差, 进而增加制冷机系统冷损. 为定量探究4~20 K 深低温区固体接触热阻, 采用GM 作为冷源, 设计了一台可同时调节压力和低温温度的固体界面接触热阻测试实验台. 利用感压纸进行接触界面压力校核, 并对温度重复性进行验证. 实验测试了不同导热介质填充情况下, 温度和压力变化时固体接触热阻的变化规律. 基于最小二乘法对实验数据进行半经验公式拟合, 获得4 ~20 K 温区不同压力加载条件下的接触热阻的定量参考.  相似文献   
7.
Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.  相似文献   
8.
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.  相似文献   
9.
The dimensionless third-order nonlinear Schrödinger equation (alias the Hirota equation) is investigated via deep leaning neural networks. In this paper, we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g. bright soliton, breather, and rogue waves) of the Hirota equation when the two types of the unperturbated and perturbated (a 2% noise) training data are considered. Moreover, we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of bright solitons.  相似文献   
10.
Natural deep eutectic solvents (NADESs) are defined as mixtures of certain molar ratios of natural compounds such as sugars, organic acids, amino acids, and organic bases that are abundant in organisms. The melting points of these mixtures are considerably lower than those of their individual ingredients and far below ambient temperature. The first publications on the NADES concept in 2011 created a great expectation regarding their potential as green solvents that could replace conventional organic solvents in a wide range of applications. This was largely because many of the drawbacks of conventional synthetic ionic liquids (ILs) and deep eutectic solvents (DESs), particularly their toxicity and environmental hazards, could be solved using NADESs. Throughout the last 7 years, the interest in NADESs has increased enormously as reflected by the exponential growth of the number of related publications. The research on NADESs has rapidly expanded particularly into the evaluation of the feasibility of their application in diverse fields such as the extraction of (targeted) bioactive compounds from natural sources, as media for enzymatic or chemical reactions, preservatives of labile compounds, or as vehicles of non–water-soluble compounds for pharmaceutical purposes. Along with the exploration of these potential applications, there have been a large number of other studies related to their physicochemical features, the search for new NADESs, the research into the interactions between NADES components or with solutes, the recovery of solutes from NADES solutions, and the ways of circumventing inherent problems of NADESs such as their high viscosity and the consequent difficulties in handling them. This article contains a review of the applications of NADESs as extraction solvents, reaction media, and preservative, providing also a perspective of their future.  相似文献   
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