Intelligent UAV Based Energy Supply for 6G Wireless Powered IoT Networks

In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Research on Network Cognition Model and Mechanism of Intelligent Information Network

Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

Research on the Development Trend of Electronic Communication Technology Based on Intelligent Network

With the rapid development of electronic communication technology,various new technical elements are constantly added to it,bringing many changes to people’s lives and work.The traditional data diversion mode can no longer truly meet the needs of actual work,and the electronic communication mode plays a huge role and occupies an important position in the communication market.Regarding how to develop and apply intelligent electronic communication technology more perfectly,there will be an overview of the specific principle of intelligent electronic communication technology,from the multi-faceted impact of electronic communication technology on human society.The article put forward the future development trend of electronic communication technology based on intelligent networks,emphasized expanding the scale of technology coverage,improved the comprehensive quality of technical products,optimized the structure of the communication industry,and formed a perfect industrial chain,so as to improve the intelligent level of electronic communication technology.

Intelligence Driven Wireless Networks in B5G and 6G Era:A Survey

As the wireless communication network undergoes continuous expansion,the challenges associated with network management and optimization are becoming increasingly complex.To address these challenges,the emerging artificial intelligence(AI)and machine learning(ML)technologies have been introduced as a powerful solution.They empower wireless networks to operate autonomously,predictively,ondemand,and with smart functionality,offering a promising resolution to intricate optimization problems.This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks.The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.

Intelligent Vehicle Auxiliary Handling System Based on the Internet of Things Technology

This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles.The system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of IoT.This intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual operation.It offers efficient and accurate handling,setting a new trend in the handling industry.Additionally,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and statuses.This allows managers to monitor the parking lot operations clearly and efficiently.This intelligent coordination greatly enhances overall work efficiency and streamlines parking management.Overall,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth operation.Looking ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.

Intelligent Resources Management System Design in Information Centric Networking

Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.

Softswitch Based Solution to Make Network Intelligent

The factors, such as the network optimization or the network amelioration by fixed telecommunication network operators, the convergence of the Personal Handy-phone System (PHS) network and the Public Switched Telephone Network (PSTN), the integration of PSTN and the Third Generation Mobile Communication (3G) Network, the broadband and multimedia based communication networks, causes the requirement for fixed network’s intelligentization. The Softswitch is a feasible approach to meet this kind of requirement. The solution to make the network comprehensively intelligent based on Softswitch is highly advantageous, which enriches communication services and promotes Fixed and Mobile Convergence (FMC).

Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects

Over the past years,the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life.However,using machine learning in intelligent networks also presents potential security and privacy threats.A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model.In this survey,we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time.We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms,and analyze the strengths and limitations of corresponding defense methods in a compact form.We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

DESIGN ISSUES FOR BIT RATE-ADAPTIVE 3R O/E/O TRANSPONDER IN INTELLIGENT OPTICAL NETWORKS

This paper reported the design and implementation of a bit rate adaptive Optical Electronic Optical(O/E/O)transponder accomplishing almost full data rate transparency up to 2.5 Gb/s with 3R(Reamplifying,Reshaping and Retiming)processing in electronic domain.Based on the chipsets performing clock recovery in several continuous bit rate ranges,a clock and data regenerating circuit self adaptive to the bit rate of input signal was developed.Key design issues were presented,laying stress on the functional building blocks and scheme for the bit rate adaptive retiming circuit.The experimental results show a good scalability performance.

Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.

Intelligent Space All-Optical Network Technology

Microwave transmission in a space network is greatly restricted due to precious radio spectrum resources. To meet the demand for large-bandwidth, global seamless coverage and on-demanding access, the Space All-Optical Network(SAON) becomes a promising paradigm. In this paper, the related space optical communications and network programs around the world are first briefly introduced. Then the intelligent Space All-Optical Network(i-SAON), which can be deemed as an advanced SAON, is illustrated, with the emphasis on its features of high survivability, sensing and reconfiguration intelligence, and large capacity for all optical load and switching. Moreover, some key technologies for i-SAON are described, including the rapid adjustment and control of the laser beam direction, the deep learning-based multi-path anti-fault routing, the intelligent multi-fault diagnosis and switching selection mechanism, and the artificial intelligence-based spectrum sensing and situational forecasting.

卷积神经网络与有限元分析在棒材轧制孔型参数仿真中的应用

为了实现智能化轧制仿真过程,提出了将卷积神经网络(CNN)与有限元分析相结合的方法,利用机器代替人工选择出最佳的轧制仿真结果,并确定最优的孔型参数.首先,将CNN用于轧件道次分类,可发现“卷积神经网络+图像预处理”测试模型精度最高,为91.84%~96%;其次,使用5种评估方法对比轧制仿真结果与理想仿真结果后可得出,均方根误差和感知哈希对仿真结果更为敏感;最后,将孔型参数朝轧制仿真结果变优方向进行修正,对比仿真结果与修正前结果后发现,第2次修正更为理想.本研究中实现了CNN对棒材轧制道次分类,利用机器识别出更优的轧制仿真结果,并确定了更适合的孔型参数,在降低人力、物力消耗,以及实现仿真智能化等方面具有重要的理论意义和实际应用价值.

通信网络的监控与优化平台在新媒体下的应用研究

在新媒体时代背景下,通信网络监控与优化平台面临着新的挑战与机遇。文章研究了在新媒体环境下,通信网络监控与优化平台的应用与发展。首先,分析新媒体给通信网络带来的流量变化、用户需求多样性以及实时性要求等特点,进而探讨监控与优化平台如何适应这些变化。其次,研究监控与优化平台在新媒体下的关键技术,包括大数据分析、人工智能算法等。这些技术为平台的智能化、自动化和高效化提供了有力支持。最后,通过实际案例分析监控与优化平台在新媒体领域的应用效果,展示其在提升网络性能、优化用户体验以及保障信息安全等方面的积极作用。文章研究对于推动通信网络监控与优化平台在新媒体时代的发展具有重要的参考价值。

5G确定性网络助力煤矿智能化的关键技术研究

介绍了5G确定性网络技术在煤矿行业的应用情况,从矿山开采设备远程控制、矿用卡车/运输车无人驾驶、井下视频通话和智能矿灯、机器人自动巡检等方面给出5G确定性网络在智慧煤矿应用场景下的建网方案,介绍了5G确定性网络的构架,探究了组网融合技术TSN、切片网络、5G LAN以及网络安全技术等新型确定性网络技术。

油气管网智能感知体系

油气管网运行管理的数字化转型和智能化运行需要感知体系提供数据作为支撑。阐述了智能感知的定义和油气管网感知体系的历程,分析了油气管网在供给侧(源)、输运侧(网)、存储侧(储)和需求侧(荷)4个环节的感知需求,从油气量、工艺量、环境量、状态量、行为量、空间量等6个方面介绍了油气管网感知的信息类型,在此基础上论述了油气管网智能感知体系的内涵和架构。最后,从提升感知能力、实现全产业链感知、增强智能化水平等方面提出了油气管网感知体系的发展建议。

基于深度学习的炉内钢坯关键点检测方法

在部分生产线上,钢坯从炉内出炉的过程中,需要依靠人眼判断钢坯是否到达指定位置,到位后再利用推钢机推动炉内的钢坯完成出炉过程。在这个过程中,人眼长时间观察摄像头屏幕容易疲劳,人工劳动强度大成本高,生产工作效率较低。针对以上问题,文中提出利用机器视觉系统替代人类视觉系统进行钢坯位置的实时定位,首先将钢坯定位问题转换为关键点检测问题,然后提出了基于ResNet网络和基于关键点分割网络(Key Point Segmentation Network,KPSN)的两种模型来进行关键点检测,最后,通过测试和分析所提出的两种方法,提出了多方法融合的关键点检测方案,降低了极端情况下误检的风险,实际应用表明,文中所提方法具有较高的鲁棒性,达到了实际应用的要求。