共查询到20条相似文献，搜索用时 46 毫秒
1.
[目的/意义]在非结构化语料集中抽取知识要素,是实现知识图谱的重要环节,本文探索了应用深度学习中的卷积神经网络（CNN）模型进行旅游领域知识关系抽取方法。[方法/过程]抓取专业旅游网站的相关数据建立语料库,对部分语料进行人工标注作为训练集和测试集,通过Python语言编程实现分词、向量化及CNN模型,进行关系抽取实验。[结果/结论]实验结果表明,应用卷积神经网络对非结构化的旅游文本进行关系抽取时能够取得满意的效果（Precision 0.77,Recall 0.76,F1-measure 0.76）。抽取结果通过人工校对进行优化后,可以为旅游知识图谱构建、领域本体构建等工作奠定基础。 相似文献
2.
[目的/意义]随着互联网数字资源的剧增,如何从海量数据中挖掘出有价值的信息成为数据挖掘领域研究的热点问题。文本大数据分类是这一领域的关键问题之一。随着深度学习的发展,使得基于深度学习的文本大数据分类成为可能。[方法/过程]针对近年来出现的图神经网络文本分类效率低的问题,提出改进的方法。利用文本、句子及关键词构建拓扑关系图和拓扑关系矩阵,利用马尔科夫链采样算法对每一层的节点进行采样,再利用多级降维方法实现特征降维,最后采用归纳式推理的方式实现文本分类。[结果/结论]为了测试该文所提方法的性能,利用常用的公用语料库和自行构建的NSTL科技期刊文献语料库对本文提出的方法进行实验,与当前常用的文本分类模型进行准确率和推理时间的比较。实验结果表明,所提出的方法可在保证文本及文献大数据分类准确率的前提下,有效提高分类的效率。 相似文献
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《Information processing & management》2022,59(3):102916
Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification. 相似文献
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Basant Agarwal Heri Ramampiaro Helge Langseth Massimiliano Ruocco 《Information processing & management》2018,54(6):922-937
This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches. 相似文献
5.
Semantic image segmentation is a challenging problem from image processing where deep convolutional neural networks (CNN) have been applied with great success in the recent years. It deals with pixel-wise classification of an input image, dividing it into regions of multiple object classes. However, CNNs are opaque models. Given a trained CNN, it is hard to tell which information encoded in the input image is important for the network to perform segmentation. Such information could be useful to judge whether a trained network learned to segment in a plausible way or how its performance can be improved.For a trained CNN, we formulate an optimization problem to extract relevant image fractions for semantic segmentation. We try to identify a subset of pixels that contain the relevant information for the segmentation of one selected object class. In experiments on the Cityscapes dataset, we show that this is an easy way to gain valuable insight into a CNN trained for semantic segmentation. Looking at the relevant image fractions, we can identify possible limits of a trained network and draw conclusions about possible improvements. 相似文献
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Convolutional neural network (CNN) and its variants have led to many state-of-the-art results in various fields. However, a clear theoretical understanding of such networks is still lacking. Recently, a multilayer convolutional sparse coding (ML-CSC) model has been proposed and proved to equal such simply stacked networks (plain networks). Here, we consider the initialization, the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC model. Inspired by these considerations, we propose two novel multilayer models: the residual convolutional sparse coding (Res-CSC) model and the mixed-scale dense convolutional sparse coding (MSD-CSC) model. They are closely related to the residual neural network (ResNet) and the mixed-scale (dilated) dense neural network (MSDNet), respectively. Mathematically, we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC model. We also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model, which gives a clear mathematical understanding of each. We implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC models. The unfolding operation can be employed for further improvement. Finally, extensive numerical experiments and comparison with competing methods demonstrate their effectiveness. 相似文献
7.
Cellular neural network (CNN) is a type of analog, nonlinear, real-time parallel processing network. The paper studies an implementation of the integrated operational amplifier used in the design of an artificial CNN. It improves the implementation of CNN proposed by Chua. A 4 x 4 artificial CNN circuit is designed, obtaining a better characteristic of a CNN connected component detector. Finally, a fast algorithm for a CNN component detector is given which proves to be very simple and quite convenient. 相似文献
8.
《Journal of The Franklin Institute》2023,360(12):9180-9197
The distributed estimation has important research significance in unmanned systems. This paper investigates the distributed estimation of unmanned surface vessel (USV) via multi-sensor collaboration and 3D object recognition, in which a Knowledge Graph (KG) is constructed to store and represent the estimation results. Kalman-consensus Filter (KCF) and convolutional neural network (CNN) are used to estimate the optimal states of objects, and recognise multiple classes of objects without designing detectors for each class of objects, respectively. The recognition efficiency is improved by dividing the data into pixel blocks whose value is the number of detection points, and a point cloud dataset in different locations and rotations is also provided. Experiments are proposed to show that our method can help the USV accurately perceive entities in the environment, which validates the effectiveness of the proposed algorithm. 相似文献
9.
《Journal of The Franklin Institute》2023,360(11):7438-7456
Hybrid quantum-classical algorithms provide a promising way to harness the power of current quantum devices. In this framework, parametrized quantum circuits (PQCs) which consist of layers of parametrized unitaries can be considered as a kind of quantum neural networks. Recent works have begun to explore the potential of PQCs as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance model discriminability of convolutional neural networks (CNNs). In CNNs, the convolutional layer uses linear filters to scan the input data followed by a nonlinear operation. Instead, we build PQCs, which are more potent function approximators, with more complex structures to capture the features within the receptive field. The feature maps are obtained by sliding the PQCs over the input in a similar way as CNN. We also give a training algorithm for the proposed model. Through numerical simulation, the proposed hybrid models demonstrate reasonable classification performance on MNIST and Fashion-MNIST (4-classes). In addition, we compare the performance of models in different settings. The results demonstrate that the model with high-expressibility ansaetze achieves lower cost and higher accuracy, but exhibits a “saturation” phenomenon. 相似文献
10.
Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems. 相似文献
11.
Chengsen Ru Jintao Tang Shasha Li Songxian Xie Ting Wang 《Information processing & management》2018,54(4):593-608
Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms. 相似文献
12.
针对钢板表面缺陷图像分类传统深度学习算法中需要大量标签数据的问题,提出一种基于主动学习的高效分类方法。该方法包含一个轻量级的卷积神经网络和一个基于不确定性的主动学习样本筛选策略。神经网络采用简化的convolutional base进行特征提取,然后用全局池化层替换掉传统密集连接分类器中的隐藏层来减轻过拟合。为了更好的衡量模型对未标签图像样本所属类别的不确定性,首先将未标签图像样本传入到用标签图像样本训练好的模型,得到模型对每一个未标签样本关于标签的概率分布(probability distribution over classes, PDC),然后用此模型对标签样本进行预测并得到模型对每个标签的平均PDC。将两类分布的KL-divergence值作为不确定性指标来筛选未标签图像进行人工标注。根据在NEU-CLS开源缺陷数据集上的对比实验,该方法可以通过44%的标签数据实现97%的准确率,极大降低标注成本。 相似文献
13.
Automatic modulation classification (AMC) is one of the core technologies in non-cooperative communication. In the complex wireless environment, it is not easy to quickly and accurately recognize the modulation styles of signals by conventional methods. The deep learning method (DLM) can deal with the problem and achieve good effects. In conventional DLMs, the length of input data is fixed. However, the signal length in communication is changing, which may not make full use of the DLMs’ input signal information to improve the recognition accuracy. In this paper, the deep multi-hop convolutional neural network (CNN) is employed to learn the time-domain signal features with different lengths. The proposed network includes the multi-hop connection rate and the receptive field extension scope to dispose of the limitation. The experiment shows that the proposed network can achieve better recognition results under the sparse multi-hop network structure. The reception field extension scope is also conducive to further improve the recognition effects. Finally, the proposed network has shorter training time and smaller parameters, which is more convenient for training the network and deploying in the existing communication system. 相似文献
14.
《Information processing & management》2023,60(1):103088
Linear equations are valuable for real-world modeling phenomena involving at least one variable. However, verifying if the procedure followed by a human for solving a linear equation was done correctly is still a complicated matter. In this paper, we propose a methodology for the automatic character recognition and revision of the solving procedure of linear equations with one unknown. First, a camera is used to acquire an image of the handwritten solving procedure. Then, the image is pre-processed, and each character and equation lines are segmented. Subsequently, a convolutional neural network (CNN) is used to conduct the character recognition stage. Finally, a comparison rule is applied to revise the solving procedure. The character recognition was verified on a 2800 image data set (2100 for training and 700 for testing), including the ten digits and four symbols: ×, +, -, /. The revision procedure was tested on a data set with 140 handwritten equations (125 for training and 15 for testing). The results revealed that we recognized handwritten characters with an accuracy of 99%, which is similar to the state-of-the-art. Moreover, our proposal revised the solving procedure with an efficiency of 86.66%. 相似文献
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本文针对传感器在自动化系统中的重要性,指出了传感器故障诊断的必要性、可行性以及实现的基本方法。根据神经网络的原理与特点,阐述了RBF神经网络的基本理论和优点,提出了一种基于RBF神经网络用于传感器故障诊断的思路和方法。 相似文献
17.
基于BP神经网络的南通市建设用地需求预测 总被引：7，自引：2，他引：5
以南通市1988年～2006年社会经济发展和建设用地数据,利用二元变量相关分析选取南通市建设用地规模扩张的驱动因子,分别采用多元回归分析和BP神经网络构建建设用地需求预测模型.在模型比较优选的基础上,预测未来南通市建设用地需求量,并应用灰色系统法结合趋势判断对预测结果合理性进行了验证.结果表明,运用全部引入法进行多元回归分析,预测模型置信程度较低;运用逐步回归法进行模型优化,多重共线性消除的同时多数驱动因子在预测模型中被剔除,造成指标选取不足;而基于BP神经网络的建设用地需求预测模型融合了各驱动因子对建设用地规模的影响,模型变异系数仅为1.78%,运用该模型可有效提高建设用地需求预测精度,计算结果较合理. 相似文献
18.
基于IPV6的网络安全入侵检测技术研究 总被引：1，自引：0，他引：1
主要研究了一种基于IPV6入侵检测技术。首先介绍了传统IPV6网络的几种网络协议,然后提出了一种采用BP神经网络技术的IPV6网络入侵检测算法。与传统网络入侵检测系统模型的对比,得到的实验数据突出了本文提出的改进型算法,有较高的优势,不管在时间上,还是在识别率上都得到了较好地提高,误检率低。 相似文献
19.
小波神经网络(Wavelet Neural Network)结合了小波变换及神经网络的优点,是一种基于知识的故障诊断方法,它不需要精确的数学模型,既具有良好的时频局部性质,又有较好的自学习能力和容错能力。本文介绍了小波网络及其在电力系统故障检测中的应用,通过EMTP仿真实验表明,小波网络与传统的人工神经网络相比,具有收敛速度快,鲁棒性强的特点,可以将小波网络应用于电力系统的故障检测。 相似文献
20.
《The Journal of High Technology Management Research》2020,31(1):100375
PurposeWith the increasing popularity of ultra-short heart rate variability (HRV) measurements being utilized with mobile devices outside of controlled, research settings, it is important to determine the proper methodology to ensure accuracy. Therefore, the purpose of this study was to examine the validity of ultra-short-term HRV metrics across three different body positions in recreationally active individuals.MethodsTwenty-six subjects (12 males: 24.1 ± 3.6 yrs., 178.6 ± 6.4 cm, 82.9 ± 8.7 kg; 15 females: 21.3 ± 1.2 yrs., 170.7 ± 10.5 cm, 71.6 ± 18.9 kg) participated in 10-min electrocardiogram recordings in the supine, seated, and standing positions. HRV analysis using a variety of time, frequency, and non-linear parameters were performed following traditional recommendations (i.e., last 5 min of each 10-min recording) and ultra-short-term recordings (i.e., 1-min epoch following a 1-min stabilization period).ResultsSlight decreases (e.g., “near perfect” to “very large”) in intraclass correlations (ICC) and increases in the limits of agreement (LOA) were noted for most of the HRV metrics as position changed to sitting and then standing. However, throughout all three positions, the highest ICC values (0.88 to 0.92) and tightest LOA (CE ± 1.96 SD) were displayed in RMSSD.ConclusionsThis study supports the use of RMSSD and SD1 for assessing HRV under ultra-short-term recordings of 1 min regardless of position. However, practitioners should be consistent with the preferred position for measurements and not use them interchangeably to reduce potential errors during long-term monitoring. 相似文献