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91.
92.
Ball screws are crucial for improving the reliability and interchangeability of transmission mechanical systems; however, existing contact measurement methods that utilise stylus contact are not efficient, which precludes their use for rapid in-situ geometry evaluation. This paper presents a vision-based two-stage method for rapid measurement of key parameters (raceway arc radii and centre distance) of ball screws. The edge contour information is extracted from the acquired image using the dual-tree complex wavelet transform and non-maximal suppression. In the matching stage, a shape-matching algorithm is used for detecting approximate geometrical centres of raceway arcs. The refinement stage, on the other hand, is implemented for acquiring precise dimensional results. Furthermore, the method of averaging multiple measurements is performed to suppress random noise. A comparative experiment is presented to validate the robustness of the proposed method. Based on experimental results, the calculated mean absolute errors in the measurement of the two raceway arc radii and the centre distance are found to be 0.0082 mm, 0.0079 mm and 0.0055 mm, respectively. This study therefore paves the way for key parameter measurement without removing ball screws. 相似文献
93.
Georgia-Ioanna Kartalou Ana Rita Salgueiro-Pereira Thomas Endres Angelina Lesnikova Plinio Casarotto Paula Pousinha Kevin Delanoe Elke Edelmann Eero Castrn Kurt Gottmann Hlne Marie Volkmar Lessmann 《International journal of molecular sciences》2020,21(23)
Therapeutic approaches providing effective medication for Alzheimer’s disease (AD) patients after disease onset are urgently needed. Previous studies in AD mouse models suggested that physical exercise or changed lifestyle can delay AD-related synaptic and memory dysfunctions when treatment started in juvenile animals long before onset of disease symptoms, while a pharmacological treatment that can reverse synaptic and memory deficits in AD mice was thus far not identified. Repurposing food and drug administration (FDA)-approved drugs for treatment of AD is a promising way to reduce the time to bring such medication into clinical practice. The sphingosine-1 phosphate analog fingolimod (FTY720) was approved recently for treatment of multiple sclerosis patients. Here, we addressed whether fingolimod rescues AD-related synaptic deficits and memory dysfunction in an amyloid precursor protein/presenilin-1 (APP/PS1) AD mouse model when medication starts after onset of symptoms (at five months). Male mice received intraperitoneal injections of fingolimod for one to two months starting at five to six months. This treatment rescued spine density as well as long-term potentiation in hippocampal cornu ammonis-1 (CA1) pyramidal neurons, that were both impaired in untreated APP/PS1 animals at six to seven months of age. Immunohistochemical analysis with markers of microgliosis (ionized calcium-binding adapter molecule 1; Iba1) and astrogliosis (glial fibrillary acid protein; GFAP) revealed that our fingolimod treatment regime strongly down regulated neuroinflammation in the hippocampus and neocortex of this AD model. These effects were accompanied by a moderate reduction of Aβ accumulation in hippocampus and neocortex. Our results suggest that fingolimod, when applied after onset of disease symptoms in an APP/PS1 mouse model, rescues synaptic pathology that is believed to underlie memory deficits in AD mice, and that this beneficial effect is mediated via anti-neuroinflammatory actions of the drug on microglia and astrocytes. 相似文献
94.
当今的多相催化研究需要新的技术和方法从原子尺度上表征活性中心结构和反应中间体。本文作者课题组近期开发了理论模拟新技术来探索催化剂活性位点结构,即基于神经网络势函数的大规模原子模拟(LASP)软件中实现的全局神经网络势函数计算方法。本文介绍了该方法可以显著降低催化体系的计算代价,而维持与密度泛函理论同一级别的计算精度,从而解决多相催化中的许多复杂问题。本文对神经网络势函数方法的实现细节和目前已实现的应用场景进行了详细介绍。神经网络势函数可以用来预测材料晶体结构,理解高压氢化条件下TiO2表面的结构演化和确定三元氧化物ZnCrO晶相中合成气制甲醇活性位点。最后文章分析了神经网络势函数的局限性和今后可能的三个研究方向,即材料性质预测、多元素体系神经网络势函数构造和化学反应拟合。 相似文献
95.
Abdelmoty M. Ahmed Reda Abo Alez Gamal Tharwat Muhammad Taha B. Belgacem Ahmad M. J. Al Moustafa 《成像科学杂志》2020,68(1):11-23
ABSTRACTArabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%. 相似文献
96.
As an unsupervised learning method, stochastic competitive learning is
commonly used for community detection in social network analysis. Compared with the
traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to
improve the accuracy and solve the problem that several parameters in stochastic
competitive learning need to be pre-set, the author improves the algorithms and realizes
improved stochastic competitive learning by particle position initialization, parameter
optimization and particle domination ability self-adaptive. The experiment result shows
that each improved method improves the accuracy of the algorithm, and the F1 score of
the improved algorithm is 9.07% higher than that of original algorithm. 相似文献
97.
Due to its outstanding ability in processing large quantity and high-dimensional
data, machine learning models have been used in many cases, such as pattern recognition,
classification, spam filtering, data mining and forecasting. As an outstanding machine
learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations,
yet in selecting qualified applicants for winning a funding is almost new. The major problem
lies in how to accurately determine the importance of attributes. In this paper, we propose a
Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify
funding applicants in to two types: approved ones or not approved ones. The FGDKNN is
based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels
by minimizing error ratio iteratively, so that the importance of attributes can be described
better. We investigate the performance of FGDKNN with Beijing Innofund. The results show
that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN,
respectively. Moreover, the FGDKNN has fast convergence time under different training
scales, and has good performance under different settings. 相似文献
98.
Single image super resolution (SISR) is an important research content in the
field of computer vision and image processing. With the rapid development of deep
neural networks, different image super-resolution models have emerged. Compared to
some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods
using traditional convolutional neural networks, SISR based on generative adversarial
networks (GAN) has achieved the most advanced visual performance. In this review, we
first explore the challenges faced by SISR and introduce some common datasets and
evaluation metrics. Then, we review the improved network structures and loss functions
of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of
different networks are analyzed by multiple comparative experiments. Finally, we
summarize the paper and look forward to the future development trends of GAN-based
perceptual SISR. 相似文献
99.
Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, overcoming the weaknesses of conventional phrase-based translation
systems. Although NMT based systems have gained their popularity in commercial
translation applications, there is still plenty of room for improvement. Being the most
popular search algorithm in NMT, beam search is vital to the translation result. However,
traditional beam search can produce duplicate or missing translation due to its target
sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural
machine translation improvements based on a novel beam search evaluation function. And
we use reinforcement learning to train a translation evaluation system to select better
candidate words for generating translations. In the experiments, we conducted extensive
experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual
corpora of NiuTrans are used in our experiments. The experiment results prove that the
proposed methods can effectively improve the English to Chinese translation quality. 相似文献
100.
The application of deep learning in the field of object detection has
experienced much progress. However, due to the domain shift problem, applying an
off-the-shelf detector to another domain leads to a significant performance drop. A
large number of ground truth labels are required when using another domain to train
models, demanding a large amount of human and financial resources. In order to avoid
excessive resource requirements and performance drop caused by domain shift, this
paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our
approach improves the cross-domain vehicle detection model from image space and
feature space. We employ objectives of the generative adversarial network and cycle
consistency loss for image style transfer in image space. For feature space, we align
feature distributions between the source domain and the target domain to improve the
detection accuracy. Experiments are carried out using the method with two different
datasets, proving that this technique effectively improves the accuracy of vehicle
detection in the target domain. 相似文献