Reviewing the Variability-Overuse Injury Hypothesis: Does Movement Variability Relate to Landing Injuries?     

Andrew D. Nordin  Janet S. Dufek 《Research quarterly for exercise and sport》2019,90(2):190-205

Purpose: Overuse injuries are common in sport, but complete understanding of injury risk factors remains incomplete. Although biomechanical studies frequently examine musculoskeletal injury mechanisms, human movement variability studies aim to better understand neuromotor functioning, with proposed connections between overuse injury mechanisms and changes in motor variability. Method: In a narrative review, we discuss the variability-overuse injury hypothesis, which suggests repeated load application leads to mechanical tissue breakdown and subsequent injury when exceeding the rate of physiological adaptation. Due to the multidisciplinary nature of this hypothesis, we incorporate concepts from motor control, neurophysiology, biomechanics, as well as research design and data analysis. We therefore summarize multiple perspectives while proposing theoretical relationships between movement variability and lower extremity overuse injuries. Results: Experimental data are presented and summarized from published experiments examining interactions between experimental task demands and movement variability in the context of drop landing movements, along with comparisons to previous movement variability studies. Conclusion: We provide a conceptual framework for sports medicine researchers interested in predicting and preventing sports injuries. Under performance conditions with greater task demands, we predict reduced trial-to-trial movement variability that could increase the likelihood of overuse injuries.  相似文献   

基于BP神经网络的突发事件政府应急管理能力评价研究     

王薇  曹亚 《科技管理研究》2018,(19)

推进国家治理体系和治理能力现代化在党的十九大报告中再次被强调,而政府应对频发的突发事件的系统化、规范化和法治化管理是推进政府治理能力现代化的重要环节。科学地评价政府的应急管理能力有助于推进政府管理能力,推动国家治理能力现代化发展。本文结合国内外应急管理的评价性研究成果,采用WSR系统方法,系统性地构建了由四个一级指标及其下54个二级指标组成的政府管理能力评价指标体系,并利用BP神经网络算法,消除人为因素的干扰性,建立科学、系统、全面的评价模型对突发事件政府应急管理能力进行综合评价。通过对长沙市政府的应急管理能力进行实际应用,表明该评价方法操作性强,对整体数据的运算性强,能科学、客观、有效地对横向政府间的应急管理能力进行比较分析和综合评价。  相似文献   

一种基于图卷积自编码模型的多维度学科知识网络融合方法     

李慧  胡吉霞 《图书情报工作》2020,64(18):114-125

[目的/意义] 针对包含单一类型知识单元的知识网络难以全面反映学科知识结构的问题，提出一种从多维度进行知识网络结构融合的方法，为学科领域知识结构挖掘提供借鉴。[方法/过程] 利用LDA及TF-IDF方法抽取学科知识单元，然后运用语义相似度和关键词共现分析方法构建3个学科知识子网络：主题网络、关键词网络和实体网络，并采用空间节点传递对齐方法对齐子网络节点，接着设计基于图卷积操作的自编码模型对知识节点进行表示，最后通过计算余弦相似度重构学科知识网络。[结果/结论] 实验部分以人工智能领域为例，构建融合主题、关键词和实体的学科知识网络并展开分析，实验结果表明，本文所提方法能有效地揭示学科领域研究内容和知识结构，为学科知识发现与组织研究提供有益参考。  相似文献   

基于DE-SVM的船舶航迹预测模型     

刘娇  史国友  杨学钱  朱凯歌 《上海海事大学学报》2020,41(1):34-39

为提高船舶航迹预测精度,解决准确建模难度大和神经网络易陷入局部最优的问题,考虑实时获取目标船AIS数据较少的特点,提出一种基于支持向量机(support vector machine,SVM)的航迹预测模型。选择AIS数据中的航速、航向和船舶经纬度作为样本特征变量;采用小波阈值去噪的方法处理训练数据;采用差分进化(differential evolution,DE)算法对模型内部参数寻优以提高模型收敛速度和预测精度。选取天津港实船某段航迹的AIS数据,比较基于DE-SVM与基于BP神经网络的航迹预测模型的仿真结果。结果表明,基于DE-SVM的航迹预测模型具有更高的预测精度,简单、可行、高效,且耗时少。  相似文献   

基于神经网络的认知无线电合作频谱感知     

薛建伟  唐亮  卜智勇 《中国科学院大学学报》2020,37(3):379-386

认知无线电是一种可用于有效缓解当前频谱资源紧张的技术，而频谱感知是认知无线电的前提。针对低信噪比情况下频谱感知性能差的问题，提出一种将信号高阶统计量、协方差矩阵特征值与神经网络相结合的合作频谱感知算法。该算法考虑到认知用户与授权用户的信道衰落情况，利用神经网络较强的多分类能力，将最大-最小特征值之比、平均-最小特征值之比以及高阶统计量作为特征参数，通过神经网络实现合作频谱感知。仿真结果表明，该算法不仅在低信噪比情况下较其他算法具有更高的频谱检测率，而且对频谱中信号的调制类型也有较高的识别率。  相似文献   

Ancient Mural Classification Method Based on Improved AlexNet Network     

Jianfang Cao  Hongyan Cui  Qi Zhang  Zibang Zhang 《文物保护研究》2020,65(7):411-423

ABSTRACT

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.  相似文献   

基于BP神经网络的共享服务平台资源信息质量评价研究——以短租类共享服务平台为例     

李全喜  徐嘉徽  魏骏巍  张健 《图书情报工作》2019,63(10):125-133

[目的/意义] 以短租类共享服务平台为例，构建共享服务平台资源信息质量评价指标体系，帮助此类平台企业高效地识别出存在信息质量问题的资源，提高平台整体的信息质量水平。[方法/过程] 首先基于信息传播学相关理论，对共享服务平台信息传播过程进行总结。然后根据共享服务平台信息传播的参与主体和访谈原始资料分析，构建共享服务平台资源信息质量评价指标体系，分为信源质量、信息内容质量和信息效用质量三个维度。最后提出基于BP神经网络的信息质量评价方法，并使用Matlab2018a软件对采集的100组样本数据进行训练和仿真验证。[结果/结论] 提出共享服务平台资源信息质量评价指标体系，并以短租类共享服务平台为例运用BP神经网络进行验证，实验证明该评价指标体系具有一定的可行性和实用性。  相似文献   

Recycling Piaget: Posthumanism and making children’s knowledge matter     

Teresa K. Aslanian 《Educational Philosophy and Theory》2018,50(4):417-427

A growing body of research incorporates children’s perspectives into the research process. If we are to take children’s perspectives seriously in education research, research methodologies must be capable of addressing issues that matter to children. This article engages in a theoretical discussion that considers how a posthuman research methodology can support such an effort. Piaget’s early and lesser known qualitative studies on children’s conception of the world are re-read along with Karen Barad’s posthuman theory, using Catherine Malabou’s concept of plasticity. Through a plastic reading of Piaget and Barad, I consider how a posthuman theoretical framework might contribute to research seeking to access children’s perspectives. Before concluding, I reflect on some ethical concerns regarding posthuman research in education.  相似文献   

基于反向随机投影的神经网络改进算法          下载免费PDF全文

许邓艳卢民荣 《福建工程学院学报》2020,(4):358-364

提出基于反向随机局部投影的神经网络效率改进算法，通过降低深度学习中的网络规模，重点解决了从“局部连接”到“全连接”和随机节点抽取时输入端节点信息丢失的问题，从而提升网络的效率。在算法中设置缩减参数，提升了算法的可伸展性，以适用于不同数据集的学习。通过数据集ISO-LET进行实验，结果表明，基于反向随机局部投影的神经网络效率改进算法的准确率、效率分别平均提升了3.48%和105.21%?在迭代20次的实验中进行了缩减参数调节实验，当参数设置为1.4时其准确率则优于传统全连接网络2.61%，效率提升了272.78%，具有明显的优势。  相似文献   

Knowledge-guided multi-granularity GCN for ABSA     

《Information processing & management》2023,60(2):103223

Aspect-based sentiment analysis aims to determine sentiment polarities toward specific aspect terms within the same sentence or document. Most recent studies adopted attention-based neural network models to implicitly connect aspect terms with context words. However, these studies were limited by insufficient interaction between aspect terms and opinion words, leading to poor performance on robustness test sets. In addition, we have found that robustness test sets create new sentences that interfere with the original information of a sentence, which often makes the text too long and leads to the problem of long-distance dependence. Simultaneously, these new sentences produce more non-target aspect terms, misleading the model because of the lack of relevant knowledge guidance. This study proposes a knowledge guided multi-granularity graph convolutional neural network (KMGCN) to solve these problems. The multi-granularity attention mechanism is designed to enhance the interaction between aspect terms and opinion words. To address the long-distance dependence, KMGCN uses a graph convolutional network that relies on a semantic map based on fine-tuning pre-trained models. In particular, KMGCN uses a mask mechanism guided by conceptual knowledge to encounter more aspect terms (including target and non-target aspect terms). Experiments are conducted on 12 SemEval-2014 variant benchmarking datasets, and the results demonstrated the effectiveness of the proposed framework.  相似文献