电液比例控制技术在快速深拉伸液压机中的应用

目前快速深拉伸液压机大多采用液压开关式控制系统,它存在着响应速度低、冲击振动大及拉伸件废品率高等问题。文章在比较了3种液压控制技术性能及特点的基础上,设计了一种快速深拉伸液压机的电液比例控制系统,使压边力的变化符合了拉伸工艺要求,滑块速度转变平稳,较好地解决了上述问题。     

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.     

基于YOLOv5s-FCS的钢材表面缺陷检测

针对传统钢材表面缺陷检测方法易出现误检、漏检和部分缺陷种类检测精度低等问题,本文设计了一种钢材表面缺陷网络YOLOv5s-FCS。首先本文引用了FReLU激活函数构建了卷积模块CBF,有效增强了网络的空间解析能力,优化了网络检测精度;其次,本文将坐标注意力机制嵌入到网络的neck部分来增强网络特征融合的能力,从而使网络能够提取更加丰富的特征信息;最后,将YOLOv5s的损失函数替换为SIoU loss,提高了预测框的回归精度。通过在NEU-DET数据集上进行消融实验、可视化对比实验,结果表明,YOLOv5s-FCS网络的mAP值达到了0.747,相较于原YOLOv5s网络提高了8.3%,相较于YOLOv3网络提高了11.8%,相较于YOLOXs网络提高了4.2%,相较于YOLOv6s提高了1.4%,验证了该方法的可行性、有效性。     

Application of Deep Eutectic Solvents in the Synthesis of Substituted 2-Mercaptoquinazolin-4(3H)-Ones: A Comparison of Selected Green Chemistry Methods

In this study, deep eutectic solvents (DESs) were used as green and eco-friendly media for the synthesis of substituted 2-mercaptoquinazolin-4(3H)-ones from different anthranilic acids and aliphatic or aromatic isothiocyanates. A model reaction on anthranilic acid and phenyl isothiocyanate was performed in 20 choline chloride-based DESs at 80 °C to find the best solvent. Based on the product yield, choline chloride:urea (1:2) DES was found to be the most effective, while DESs acted both as solvents and catalysts. Desired compounds were prepared with moderate to good yields using stirring, microwave-assisted, and ultrasound-assisted synthesis. Significantly, higher yields were obtained with mixing and ultrasonication (16–76%), while microwave-induced synthesis showed lower effectiveness (13–49%). The specific contribution of this research is the use of DESs in combination with the above-mentioned green techniques for the synthesis of a wide range of derivatives. The structures of the synthesized compounds were confirmed by ¹H and ¹³C NMR spectroscopy.     

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.     

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.     

地应力测量非弹性应变恢复法在某金属矿的应用

为获得某金属矿深部岩体地应力分布特征,采用非弹性应变恢复法,选取该矿同一勘探线附近距地表埋深615 m、665 m、715 m三个水平进行现场地应力测量。通过分析3个水平各测点地应力测量结果,得到该矿区地应力分布规律。该矿区最大主应力和最小主应力均为近水平主应力,中间主应力为近垂向的应力;3个水平的最大水平主应力方向基本一致,方位角为101°～130°。主应力的大小随深度呈线性增长。近水平应力大于近垂向应力,表明该矿浅部地壳应力占主导地位的是水平构造应力。两个相邻样品的非弹性应变恢复法地应力测量值平均差系数最大为8.33%,表明其结果具有较好的一致性,验证了非弹性应变恢复法的可靠性。