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81.
人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。 相似文献
82.
针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。 相似文献
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85.
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
86.
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
87.
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
88.
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
89.
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
90.
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