On Hyponymy and Semantic Fields

This paper aims at elaborating on the semantics. The concept of hyponymy is mentioned first. Then the concept of semantic fields is mentioned. In order to make a comparison between hyponymy and semantic fields, there are some examples in the paper.

Bilateral U-Net semantic segmentation with spatial attention mechanism

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial Attention(APSA)module compared to the Attenuated Spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

COVID-19 Lexicon in English News Reports Based on the Theory of Semantic Field

Coronavirus disease,or simply COVID-19,has affected many regions worldwide.The pandemic has caused great losses from all walks of life.Millions of people have died from the virus.In order to facilitate people’s understanding of COVID-19,the present study adopts the theory of semantic field to analyze the COVID-19 lexicon that appeared in China Daily,an authoritative international daily newspaper issued by China.A total of 100 pieces of English news issued by China Daily have been randomly selected for this research.According to the theory of semantic field in structural linguistics,the meaning of a word cannot stand alone,but come into being with the meanings of its related words.Therefore,it is reasonable to try to understand COVID-19 as thoroughly as possible with relevant words,which form its semantic field.

Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold

Latent Semantic Analysis involves natural language processing techniques for analyzing relationships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and terms) called semantic topics. These semantic topics assist search engine users by providing leads to the more relevant document. We develope a novel algorithm called Latent Semantic Manifold (LSM) that can identify the semantic topics in the high-dimensional web data. The LSM algorithm is established upon the concepts of topology and probability. Asearch tool is also developed using the LSM algorithm. This search tool is deployed for two years at two sites in Taiwan: 1) Taipei Medical University Library, Taipei, and 2) Biomedical Engineering Laboratory, Institute of Biomedical Engineering, National Taiwan University, Taipei. We evaluate the effectiveness and efficiency of the LSM algorithm by comparing with other contemporary algorithms. The results show that the LSM algorithm outperforms compared with others. This algorithm can be used to enhance the functionality of currently available search engines.

基于改进SwiftNet的堆场图像实时分割网络

在堆场环境下,实时图像语义分割可以提供直观的场景类别信息。为节约工控机等边缘设备的硬件资源以及为多源信息融合提供图像语义类别信息,提出一种轻量化的实时语义分割网络模型。首先提出基于空间注意力引导的上采样融合模块,通过引入空间注意力和残差注意力结构设计一种轻量化的解码器,在上采样过程中还原空间细节,抑制冗余信息,进而融合不同来源的特征图;其次提出一种轻量化的级联空洞空间金字塔模块,利用级联的空洞卷积单元增大网络感受野,有效提取多尺度特征;最后使用通道分离、通道混洗、通道池化等操作,降低多尺度聚合过程中的计算开销。在公开数据集Camvid上,该模型的平均交并比(MIoU)为70.1%,推理速度为146.3帧/s,分割精度和推理速度优于ENet、ICNet等模型,消融实验结果也证明了所提各模块的有效性;在实际堆场图像数据集上,该模型的MIoU为93.5%,推理速度为123.8帧/s,证明模型结构具有良好的泛化性能。

基于改进DeeplabV3+的水面多类型漂浮物分割方法研究

【目的】为解决传统图像处理方法鲁棒性差、常用深度学习检测方法无法准确识别大片漂浮物的边界等问题,【方法】提出一种基于改进DeeplabV3+的水面多类型漂浮物识别的语义分割方法,提高水面漂浮的识别能力。对所收集实际水面漂浮物进行分类,采用自制数据集进行对比试验。算法选择xception网络作为主干网络以获得初步漂浮物特征,在加强特征提取网络部分引入注意力机制以强调有效特征信息,在后处理阶段加入全连接条件随机场模型,将单个像素点的局部信息与全局语义信息融合。【结果】对比图像分割性能指标,改进后的算法mPA(Mean Pixel Accuracy)提升了5.73%,mIOU(Mean Intersection Over Union)提升了4.37%。【结论】相比于其他算法模型,改进后的DeeplabV3+算法对漂浮物特征的获取能力更强,同时能获得丰富的细节信息以更精准地识别多类型水面漂浮物的边界与较难分类的漂浮物,在对多个水库场景测试后满足实际水域环境中漂浮物检测的需求。

基于改进UNet3+的岩心图像颗粒提取算法

在石油勘探过程中,岩心颗粒是研究地质层序、评估油气含量以及认识地质构造的有效资料,对岩心颗粒图像进行颗粒提取有利于地质研究人员后续的深入分析.岩心颗粒图像通常存在颗粒边缘模糊、背景与颗粒色彩复杂的问题.为了改善岩心颗粒提取的效果,本文设计了一种基于改进UNet3+的岩心图像颗粒提取算法.该算法在UNet3+的每个编码层后加入感受野模块(RFB)来扩大网络的感受野,从而有效地解决网络因感受野受限而导致的分割精度低的问题,并在RFB模块后嵌入了卷积块注意力模块(CBAM)使网络更加精确地聚焦于目标区域,提高目标区域的特征权重.实验结果表明,改进后的算法在岩心颗粒图像上具有良好的分割效果,相比原始UNet3+网络,分别在mIoU、mPA和FWIoU上提升了5.43%、2.99%和5.34%.

基于深度学习的茶嫩芽分割与采摘点定位方法研究

为实现茶嫩芽快速识别与采摘点定位,研究一种轻量级深度学习网络实现茶嫩芽分割与采摘点定位。采用MobileNetV2主干网络与空洞卷积相结合,较好地平衡茶嫩芽图像分割速度与精度的矛盾,实现较高分割精度的同时,满足茶嫩芽快速识别的要求,并设计外轮廓扫描与面积阈值过滤相结合的采摘点定位方法。试验表明:所提出的茶嫩芽分割算法在单芽尖及一芽一叶数据集中精度优异,平均交并比mIoU分别达到91.65%和91.36%;在保持高精度的同时,模型复杂度低,参数量仅5.81 M、计算量仅39.78 GFOLPs;在单芽尖、一芽一叶及一芽两叶数据集中各随机抽取200张图片进行采摘点定位验证,定位准确率分别达到90.38%、95.26%和96.60%。

Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

Image semantic segmentation is an essential technique for studying human behavior through image data.This paper proposes an image semantic segmentation method for human behavior research.Firstly,an end-to-end convolutional neural network architecture is proposed,which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network;then jump-connected convolution is used to classify each pixel in the image,and an image semantic segmentation method based on convolu-tional neural network is proposed;and then a conditional random field network is used to improve the effect of image segmentation of hu-man behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field im-age is proposed.Finally,using the proposed image segmentation network,the input entrepreneurial image data is semantically segmented to obtain the contour features of the person;and the segmentation of the images in the medical field.The experimental results show that the image semantic segmentation method is effective.It is a new way to use image data to study human behavior and can be extended to other research areas.

基于深度学习的田间玉米幼苗与杂草语义分割研究

为实现复杂田间背景下玉米幼苗和杂草的准确分割,提出了一种基于改进ResNet的语义分割网络模型.首先,对骨干网络进行调整,在保证感受野不变的情况下降低计算量,提高模型的分割精度;其次,引进空洞空间金字塔池化模块,增强模型对多尺度目标上下文信息和全局上下文信息的获取能力;最后,引入条带池化模块补充和完善上下文信息,增强全局语义信息表达.实验结果表明,该模型在自建数据上获得85.3%的平均交并比.对田间复杂环境下玉米幼苗与杂草具有良好的分割效果和泛化能力,研究结果为智能除草设备提供一定的参考.

时空语义数字底板技术研究及其在物流领域的应用

随着数字孪生在各行各业的推进,传统的数字底板已经不能满足更加智能的时空还原和推演需求。提出了时空语义数字底板的概念,结合物流的应用场景,阐述了时空语义数字底板的功能框架和分层模块功能,从数据和知识双轮驱动的角度,构建了数据驱动画像和空间语义知识图谱,并对地址切分和融合技术、时空语义知识图谱等关键技术进行了分析。最后,介绍了时空语义数字底板在智慧物流中的应用,展示了其时空还原和时空推演的能力。

汉语常用词演变研究应注意的几个问题

汉语常用词演变研究是汉语词汇史研究的热点之一,研究成果颇多,但也存在一些不足之处有待改进。其中,有五个问题值得格外注意:一是语料的可靠性、有效性和全面性;二是全面收罗概念场成员;三是充分考虑书写形式;四是树立历史发展观;五是在更开阔的视野下考察常用词演变。希望通过这一探讨,能够对今后汉语常用词演变的深入研究有所裨益。

基于BERT预训练与混合神经网络的中文语义识别算法设计

针对现阶段电力智能客服沟通效率低且语义理解能力不佳的问题,文中基于BERT预训练模型和混合神经网络提出了一种中文语义识别算法。该算法使用BERT模型进行词嵌入表示,在得到深度编码信息的同时还可以获取上下文联系信息。通过将Bi-GRU、注意力机制以及CRF模型进行融合,使其能够处理基于上下文的词向量。同时构建的混合神经网络也可以捕获词向量的多维特征信息,进而全面提升模型的意图识别及中文语义理解能力。在实验测试中,所提算法的意图识别准确率与F1值相较于基线算法分别提升了11.3%和6.6%,表明对语料的预训练可以有效提升模型语义识别的能力。

基于知识图谱的柴油发动机故障诊断研究与系统设计

由于高速公路施工项目工期短、成本高等原因,高速公路施工现场的柴油发动机在发生故障时,需要得到及时的故障诊断和故障处理。通过BiLSTM-CRF模型实现故障实体抽取和关系抽取,利用结构化的语义网络来描述柴油发动机故障知识,以此构建柴油发动机故障领域知识图谱。同时,结合贝叶斯网络实现故障原因推理以对其知识图谱进行补全,还设计了基于知识图谱的柴油发动机故障诊断系统,以全面提升高速公路施工现场工程机械的维修效率。

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

Exploiting PLSA model and conditional random field for refining image annotation

This paper presents a new method for refining image annotation by integrating probabilistic latent semantic analysis(PLSA) with conditional random field(CRF).First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores,and then model semantic relationship among the candidate annotations by leveraging conditional random field.In CRF,the confidence scores generated by the PLSA model and the Flickr distance between pairwise candidate annotations are considered as local evidences and contextual potentials respectively.The novelty of our method mainly lies in two aspects:exploiting PLSA to predict a candidate set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation.To demonstrate the effectiveness of the method proposed in this paper,an experiment is conducted on the standard Corel dataset and its results are compared favorably with several state-of-the-art approaches.

Application of Field Theory on Memorizing Business English Lexicons

Since Reform and Opening, the international business activities in China have become more and more frequent,hence,the importance of business English goes without saying. However, due to its lexicons is complexity and specificity, it's really a struggle matter for business English learners to memorize these lexicons. Through analyzing features of business English lexicons and establishing appropriate semantic field, business English learners could memorize these lexicons more effectively.

结合全局注意力机制的实时语义分割网络

针对轻量化网络结构从特征图提取有效语义信息不足,以及语义信息与空间细节信息融合模块设计不合理而导致分割精度降低的问题,本文提出一种结合全局注意力机制的实时语义分割网络(global attention mechanism with real time semantic segmentation network,GaSeNet)。首先在双分支结构的语义分支中引入全局注意力机制,在通道与空间两个维度引导卷积神经网来关注与分割任务相关的语义类别,以提取更多有效语义信息;其次在空间细节分支设计混合空洞卷积块,在卷积核大小不变的情况下扩大感受野,以获取更多全局空间细节信息,弥补关键特征信息损失。然后重新设计特征融合模块,引入深度聚合金塔池化,将不同尺度的特征图深度融合,从而提高网络的语义分割性能。最后将所提出的方法在CamVid数据集和Vaihingen数据集上进行实验,通过与最新的语义分割方法对比分析可知,GaSeNet在分割精度上分别提高了4.29%、16.06%,实验结果验证了本文方法处理实时语义分割问题的有效性。