A survey on semantic communications:Technologies,solutions,applications and challenges

Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.

Digital Application Objectives and Benefit Analysis of BIM Technology in Large-Scale Comprehensive Development Projects

This paper discusses the digital application and benefit analysis of building information model(BIM)technology in the large-scale comprehensive development project of the Guangxi headquarters base.The project covers a total area of 92,100 square meters,with a total construction area of 379,700 square meters,including a variety of architectural forms.Through three-dimensional modeling and simulation analysis,BIM technology significantly enhances the design quality and efficiency,shortens the design cycle by about 20%,and promotes the collaboration and integration of project management,improving the management efficiency by about 25%.During the construction phase,the collision detection and four-dimensional visual management functions of BIM technology have improved construction efficiency by about 15%and saved the cost by about 10%.In addition,BIM technology has promoted green building and sustainable development,achieved the dual improvement of technical and economic indicators and social and economic benefits,set an example for enterprises in digital transformation,and opened up new market businesses.

CFSA-Net:Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

Traditional models for semantic segmentation in point clouds primarily focus on smaller scales.However,in real-world applications,point clouds often exhibit larger scales,leading to heavy computational and memory requirements.The key to handling large-scale point clouds lies in leveraging random sampling,which offers higher computational efficiency and lower memory consumption compared to other sampling methods.Nevertheless,the use of random sampling can potentially result in the loss of crucial points during the encoding stage.To address these issues,this paper proposes cross-fusion self-attention network(CFSA-Net),a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.At the core of this network is the incorporation of random sampling alongside a local feature extraction module based on cross-fusion self-attention(CFSA).This module effectively integrates long-range contextual dependencies between points by employing hierarchical position encoding(HPC).Furthermore,it enhances the interaction between each point’s coordinates and feature information through cross-fusion self-attention pooling,enabling the acquisition of more comprehensive geometric information.Finally,a residual optimization(RO)structure is introduced to extend the receptive field of individual points by stacking hierarchical position encoding and cross-fusion self-attention pooling,thereby reducing the impact of information loss caused by random sampling.Experimental results on the Stanford Large-Scale 3D Indoor Spaces(S3DIS),Semantic3D,and SemanticKITTI datasets demonstrate the superiority of this algorithm over advanced approaches such as RandLA-Net and KPConv.These findings underscore the excellent performance of CFSA-Net in large-scale 3D semantic segmentation.

An improved pulse coupled neural networks model for semantic IoT

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

Large-Scale Ontology Development and Agricultural Application Based on Knowledge Domain Framework

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management- the knowledge domain framework （KDF）, and introduces an integrated development environment （IDE） named large-scale ontology development environment （LODE）, which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the described briefly. principle of knowledge domain framework and LODE was

PDBSCAN: Parallel DBSCAN for Large-Scale Clustering Applications

Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.

Semantic Features and Applications in Translation of English Words in Pairs

English words in pairs are a special form of English idioms, which have different kinds and are used widely. For English learners, words in pairs are one of the difficult points. This paper discusses their form patterns, semantic relations, grammatical functions, rhetoric features and their application in translation. Its purpose is to help learners understand and use them accurately and correctly so as to improve language expressing ability.

The Application of Contextualism of Semantics Analysis to ELT in Chinese Vocational Schools

According to the increasingly rising problems of the modern English language teaching and the usual method of English lan-guage teaching in vocational school,this paper probes into four questions about these issues:the phenomena of today's English teach-ing,the knowledge about contextualism and semantics analysis,the application of contextualism of semantics analysis to ELT in Chinesevocational Schools.This paper broadens the horizons of people with the newly combination of linguistic with English language teaching,highlighting a new English teaching skills for the followers.

The English Subtitle Translation of Quanzhou’s World Heritage Application Documentary: A Frame Theory Perspective

Based on Fillmore’s frame theory, this paper examines the translator’s cognitive frame operation in the English subtitle translation of Quanzhou’s World Heritage application documentary. It analyzes and discusses how the translator maximizes translation effect between the source language and the target language, so as to enhance the readability of documentary subtitles and promote the spread of traditional Chinese culture. The findings are as follows: Firstly, in the translation process, the frame operations used by the translator include frame correspondence, intra-frame operations, frame replacement, frame perspective shift and frame transplantation. Secondly, cultural documentaries are full of culture-loaded words, for which the translator usually uses frame transplantation. Thirdly, due to the instantaneous nature of subtitles, intra-frame operations and frame replacement are applied in terms of cultural background of the target language audience. Besides, in order to enhance the audience’s reading experience, there may be some changes of narrative perspective in the English translation of cultural documentaries.

Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis

With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.

Efficient Routing Protection Algorithm in Large-Scale Networks

With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become critical.However,a large number of relevant studies have shown that network failures occur on the Internet inevitably and frequently.The current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network failures.During the reconvergence process,the packets may be lost because of inconsistent routing information,which reduces the network’s availability greatly and affects the Internet service provider’s(ISP’s)service quality and reputation seriously.Therefore,improving network availability has become an urgent problem.As such,the Internet Engineering Task Force suggests the use of downstream path criterion(DC)to address all single-link failure scenarios.However,existing methods for implementing DC schemes are time consuming,require a large amount of router CPU resources,and may deteriorate router capability.Thus,the computation overhead introduced by existing DC schemes is significant,especially in large-scale networks.Therefore,this study proposes an efficient intra-domain routing protection algorithm(ERPA)in large-scale networks.Theoretical analysis indicates that the time complexity of ERPA is less than that of constructing a shortest path tree.Experimental results show that ERPA can reduce the computation overhead significantly compared with the existing algorithms while offering the same network availability as DC.

A route for large-scale preparation of multifunctional superhydrophobic coating with electrochemically-modified kaolin for efficient corrosion protection of magnesium alloys

Superhydrophobic coating has been widely studied for its great applicational potential, such as for corrosion protection of magnesium alloys while it has been restrained by expensive materials, sophisticated preparation process and infirm rough structures. In this study, the electrochemical method was adopted by using a two-electrode system for rapid hydrophobic modification to obtain superhydrophobic kaolin.By mixing the modified superhydrophobic kaolin with commercial epoxy resin and polydimethylsiloxane glue, a paint can be formed and easily used on various substrates for preparation of superhydrophobic coating via spraying method. The influence factors on wettability of the modified kaolin and the mixing ratio of each component of the coating were explored. Also, the wettability, durability and anticorrosion of the prepared coating were evaluated comprehensively. The coating was able to maintain superhydrophobic after immersed in HCl solution at pH 1, the NaOH solution at pH 14, and 3.5 wt.% NaCl solution for 16, 21, 30 days, respectively. In addition, the coating exhibited 4A grade adhesion, high hydrophobicity after abraded for 200 cycles on a 600-mesh sandpaper with 100 g weight, and 99.86% anticorrosion efficiency after soaked in 3.5 wt.% NaCl solution for 20 days, demonstrating a good robustness and anti-corrosion property. Furthermore, the coating showed good transparency, flexibility and was easy to make in a large scale by the spraying method, which is of great significance to promote the practical application of superhydrophobic coatings and the anticorrosion Mg alloys.

Application of Bioreactor in Stem Cell Culture

Stem cells (SCs), the undifferentiated biological cells, have the infinite capacity to self-renew and the pluripotent ability to differentiate. SCs and their derived products offer great promise for biomedical applications such as cell therapy, tissue engineering, regenerative medicine and drug screening. However, the clinical applications of SCs require a large amount of SCs with high quality and the number of SCs from their tissue resources is very limited. Large-scale expansion is needed to generate homogeneous SCs with good biological characteristics for clinical application. This necessitates a bioreactor system to provide controllable and stable conditions for stem cell (SC) culture. Traditional methods of bioreactor for maintenance and expansion of cells rely on two-dimensional (2-D) culture techniques, leading to loss self-renewal ability and differentiation capacity upon long-term culture. New approaches for SC expansion with bioreactor employ three-dimensional (3-D) cell growth to mimic their environment in vivo. In this review, we summarize the application of bioreactors in SC culture.

The Jena-Based Ontology Model Inference andRetrieval Application

Ontology as an important representation model of semantic web has valuable application. A new ontology model on the basis of Computer Graphics (CG) knowledge is proposed, called CG ontology model. The protégé is used to build this ontology model conveniently. The Jena API is applied to store CG owl documents in MySQL, set inference rule and achieve search queries on the ontology database. Finally, the Jena-based ontology model retrieval system is developed.

Semantic Link Network Based Knowledge Graph Representation and Construction

A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of knowledge triples,it is difficult to directly display to researchers.Semantic Link Network is an attempt,and it can deal with the construction,representation and reasoning of semantics naturally.Based on the Semantic Link Network,this paper explores the representation and construction of knowledge graph,and develops an academic knowledge graph prototype system to realize the representation,construction and visualization of knowledge graph.

New Applied Research on Semantic Electronic Thematic Words

随着计算机网络与信息技术的迅速发展 ,广大用户对网上资源信息检索的质量提出更高要求 ,其关键因素是信息的自动处理和控制能力 ,电子叙词表建构及其计算机管理是提高信息检索质量的重要保证 .文中建立了叙词表、叙词关系表、停用词表以及范畴索引、词族索引 ,并给出了具体实现的数据结构与算法 .实践表明 。

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.

城市竣工测绘典型要素语义分割PointNet++深度学习模型适用性分析

处理三维激光扫描仪获取的城市竣工测绘点云场景数据的传统方法存在较多局限性,无法适应信息化社会对产品高效处理的需求。基于此,本文分析了城市竣工测绘点云场景分类需求,研究了利用深度学习网络模型对点云场景进行自动化处理的方法。首先,对输入的城市竣工测绘数据进行预处理,以实现点云降采样、去噪、地面点与非地面点分割;然后,人工标注5个区域场景数据毫米级标签,进行数据增强;最后,测试PointNet++网络在城市竣工测绘点云场景下的语义分割性能和效果。测试结果表明,在少量样本下,PointNet++网络可以较好地实现城市竣工测绘点云场景的激光点云语义分割,总体mIoU达73.06%,能够满足城市竣工测绘点云语义自动化分割需求,为城市竣工测绘点云数据处理提供了新思路。

文本图像生成技术在视觉传达设计中的应用

文本图像生成技术可以通过输入自然语言文本进行图像生成操作,输出图像精度高、样本量丰富且生成内容与文本一致性强,在视觉传达设计中有巨大应用潜力。该文在分析视觉传达设计图像生成需求基础上,解析文本生成图像的技术原理,探索文本生成图像技术在视觉传达设计中的应用路径,研究成果可以辅助设计师提高工作效率,激发创意灵感,为视觉传达设计视觉创作提供实践技术参考。

Proposal for Energy Consumption Reduction between Connected Objects in a Network Running on MQTT Protocol

The “Internet of Things” (IoT) refers to a set of intelligent “objects” that can communicate with each other directly or through a network. The IoT is the embodiment of the idea that everything can be connected anywhere and at any time. The concept can be applied to sectors such as e-health, e-government, automotive, geographic information systems, remote sensing, home networking, e-commerce and climate change mitigation. Unlike the Internet, the IoT has its own constraints, notably those linked to heterogeneity. This divergence is linked to different protocols, technologies and algorithms implemented in these connected objects for their interconnection. It should be noted that IoT devices can communicate with each other using different protocols and dedicated M2M (Machine to Machine) communication technologies. The aim of this work is to find solutions for optimising energy consumption during data exchanges between connected objects, with respect to certain constraints by using firstly this exchange for only Message Queuing Telemetry Transport (MQTT) and secondly the combination of the MQTT protocol and the Constrained Application Protocol (CoAP) protocol to check the quantity of the energy optimized. The MQTT protocol, for example, is one of the most widely used protocols for connected objects. Admittedly, this protocol consumes less energy, but in the situation of a very large number of users, the problem of saturation inevitably arises. In this article, we propose a solution of optimising energy consumption by combining the MQTT protocol with the CoAP protocol which can allow to use the standby mode contrary to the use of MQTT where the broker is always being turning. This solution has not yet been implemented but is being discussed. In this article, we’re going to use the joulemeter which is an application developed by Microsoft to measure and estimate the energy consumption of computers and applications. In our case, we take the example of the “Service Broker for network connections” of the Windows’s 10 Operating System, in my own computer to show the difference between the consumption of energy without the standby mode and with standby mode, because with the MQTT, the Broker’s MQTT is always on. Now, with the combination MQTT and CoAP, it is possible that we have standby mode and to compare these two cases in term of consumption of an energy. And to do it, we must use the joulemeter that we installed in our computer to simulate it. This is achieved by using the CoAP protocol combined with the MQTT protocol. The aim of our work is to reduce energy consumption in order to solve the problem of saturation of the MQTT by linking it to CoAP protocol by using Joulemeter mentioned above.