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Intellectualization and unmanned manufacturing have been an inevitable trend in industrial development. The landing of intelligent applications is one of the current challenges in the industry. Due to the hierarchical architecture of the industrial automation pyramid, traditional programmable logic controllers (PLCs) that are usually employed in the field cannot cooperate with artificial intelligence (AI) algorithms that require massive data and computing resources. Therefore, it is necessary to research the virtualization of traditional PLCs as dockers, which can be deployed in the cloud, edge, or field. Cloud PLCs can be easily integrated with AI, big data, and cloud computing to achieve intelligent decision-making and control and break down data islands. The visual sorting system has attracted increasing attention for its ability to accurately detect the position of objects. Many deep learning–based methods have achieved remarkable performance in computer vision. Additionally, the requirement of a network is fundamental for guaranteeing data transmission with low latency and high reliability. The combination of 5G and time-sensitive networking (TSN) can achieve the deterministic transmission of several industrial applications. According to the above challenges, joint control between cloud PLCs of low-level devices and visual sorting systems in a reliable network is critical and has industry potential. In this study, we propose a deep learning–based material recognition and location system with a cloud PLC, which is demonstrated in a 5G-TSN network. First, traditional PLC is virtualized to realize flexible PLC function deployment in the field and cloud. Second, we establish a cloud-based AI platform and design a You only look once v5 (YOLOv5)-based object detection algorithm to locate the position and recognize the types of materials to obtain pixel coordinates. Third, the camera calibration method is used to transform pixel and world coordinates, and the material information consists of the world coordinates, types, and timestamps that are sent to cloud PLC. Finally, the commands are transmitted by the 5G-TSN environment from cloud PLC to the low-level devices for real-time control of the multi-crane cooperative. We establish an experimental system to demonstrate the significance and effectiveness of the proposed scheme, which synergistically controls multi-crane operation. The mean average precision (mAP) of material location is up to 99.65%, sorting accuracy reaches 96.67%, and the average consuming time is 25.99 s, which meets the requirements of low latency and high precision in industrial applications.

Communication and sensing integrated networks (CSINs) refer to the ability of physical digital space perception and ubiquitous intelligent communication at the same time. These networks realize the perception and cooperative communication of multidimensional resources through the cooperative work of communication and sensing resources and have the ability of intelligent interaction and processing of new information flow. First, this study proposes the technical architecture of an enhanced CSIN (E-HFWN), studies its key technologies and performance indicators, and explains the air interface technology, including frame structure design, carrier aggregation, channel detection, physical skyline mapping, beamforming and management, resource allocation and scheduling. In the resource allocation scheme, an actor-critic reinforcement learning (RL) framework is used to divide the wireless resources. The goal is to maximize the amount of mutual information (MI) and minimize the end-to-end delay of the sensing terminal. Then, the performance of the E-HFWN is tested, including numerical simulation of wireless resource management, system peak rate, capacity, end-to-end delay and communication perception waveform sidelobe ratio. Finally, from the results of the E-HFWN index test, the E-HFWN is further enhanced on the basis of 5G mmWave. The enhanced sensing function can provide a priori information for the optimal and rapid scheduling of distributed computing power and provide richer data sources for artificial intelligence (AI) services and applications to enhance the robustness of the training model. The E-HFWN can contribute to the development of technologies related to 6G synaesthesia computing integrated networks, promote the consensus between academia and industry.

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

This paper takes the specific environment covered by vegetation as the research object, carries out modeling and analysis, takes the large-scale fading model of wireless channel as the basis of data processing, researches the transmission law of electromagnetic wave in a typical vegetation environment, which can be divided into four situations. The signal attenuation in each case is theoretically derived and numerically simulated. From the view point of supporting vegetation environment channel, the large-scale channel measurement system is built to meet the actual needs, such as bandwidth, frequency, vegetation coverage, etc. the final vegetation environment channel model under the large-scale fading model is obtained. The results show that the path gain of four scenarios respectively are 81.3 dB, 36.5 dB, 1.6 dB, 1.5 dB, the value of path gain index is within the range of 2~3.5, four scenarios shadow fading standard deviation values are 7.1, 4.8, 10.1, 9.2, reflects the change of received power at the point caused by random factors such as reflection, absorption and scattering. In addition, the proposed channel model improves the gain about 15% compared with the tradition SUI model within vegetation coverage scene. The design process of the proposed model is carried out in the order of "studied the existing foundation → analyzed the existing problems → proposed the optimization scheme → simulation and verification results → actual measurement system". The advantage of paper's method is that, when the signal frequency, transceiver distance, antenna height and vegetation environment characteristic parameters are given, the statistical analysis results of wireless channel data are obtained. The purpose of the proposed work establishes a signal propagation prediction model under the vegetation environment, realizes a theoretical basis for channel simulation, and provides the basis of anti-fading technologies.

To solve the deterministic transmission problem of periodic and time-triggered industrial traffics in wireless environment， a joint optimization mechanism of 5G and time sensitive networking based on radio channel information was considered.Based on improved traffic process architecture in time sensitive network， the traffics bearing on radio channel with worse quality could be processed in priority.In 5G networks， the relationship among radio channel quality， resource numbers and the maximum transmission times over radio was modeled， and the maximum transmission times satisfied data transmission reliable probability could be computed from the model，then the transmission time budget in 5G system for multiple industrial traffics bearing on diverse radio resources could be adjusted dynamically by retransmission factors.Simulation results demonstrate that the proposed joint scheduling mechanism achieves less TSN transmission delay of industrial traffics bearing on worse quality radio resources and can eliminate jitter caused by retransmission due to variation of 5G radio channel， which can guarantee end-to-end deterministic delay for industrial traffics.

Intelligent manufacturing is a challenging and compelling topic in Industry 4.0. Many computer vision (CV)-based applications have attracted widespread interest from researchers and industries around the world. However, it is difficult to integrate visual recognition algorithms with industrial control systems. The low-level devices are controlled by traditional programmable logic controllers (PLCs) that cannot realize data communication due to different industrial control protocols. In this article, we develop a multi-crane visual sorting system with cloud PLCs in a 5G environment, in which deep convolutional neural network (CNN)-based character recognition and dynamic scheduling are designed for materials in intelligent manufacturing. First, an YOLOv5-based algorithm is applied to locate the position of objects on the conveyor belt. Then, we propose a Chinese character recognition network (CCRNet) to significantly recognize each object from the original image. The position, type, and timestamp of each object are sent to cloud PLCs that are virtualized in the cloud to replace the function of traditional PLCs in the terminal. After that, we propose a dynamic scheduling method to sort the materials in minimum time. Finally, we establish a real experimental platform of a multi-crane visual sorting system to verify the performance of the proposed methods.

Quantum key distribution (QKD) promises to deliver secure keys, which can be applied for security demands in optical networks by using cost-efficient and scalable lightpaths. To achieve such secure communication, the QKD integrated with optical networks has become a promising scenario to provide key provisioning services in optical networks. As an inevitable problem, the occurrence of failures becomes a challenge for the resiliency of the network. In that context, this paper studies the resilient QKD-integrated optical networks against single link failure. By analyzing and quantifying the key provisioning services, we constructed the secret-key flow model (SKFM) for the failure-affected and failure-unaffected cases. Based on the SKFM, a secret-key recovery strategy (SKRS) including three algorithms (i.e., one-path recovery method (OPRM), multi-path recovery method (MPRM), and time window-based recovery method (TWRM)) is designed to recover failure-affected key provisioning services in the network. The simulation work has been conducted to evaluate the performance of OPRM, MPRM, and TWRM in terms of key-service recovery ratio, secret-key recovery ratio, wavelength consumption ratio, and secret-key consumption ratio. Numerical results show that the three algorithms can recover failure-affected key provisioning services effectively, i.e., the MPRM outperforms the OPRM and the TWRM outperforms the MPRM. Better recovery can be realized by sacrificing more wavelength and secret-key resources, which are also required for the delivery of the QKD in the network. Thus, a trade-off can be achieved between the recovery of key provisioning services and the delivery of the QKD on wavelength resources and secret-key resources.

In this paper, a mathematical model based on spherical differential unit cell is proposed as a model for studying seasonal freeze-thaw soil space infinitesimal differential unit cell. From this model, the basic equations of permafrost moisture and heat flow motion are directly derived, then the linked equations form the permafrost water-heat coupled transport model. On this basis, the one-dimensional seasonal permafrost water-heat transport equation is derived. The model reduces the original spatial three-variable coordinate system (parallel hexahedron) into a coupled equation with a single spherical radius (R) as the independent variable, so the iterations of the numerical simulation algorithm is greatly reduced and the complexity is decreased. Finally, the model is used to simulate the seasonal freeze-thaw soil in the ShiHeZi region of Xinjiang, China. The principle of the simulation is to collect the soil temperature and humidity values of the region in layers and fixed-points using a homemade freeze-thaw soil sensor, after that we solve it by numerical calculation using MATLAB. The analysis results show that the maximum relative error of the model we proposed is 4.36, the minimum error is 0.98, and the average error is 2.515. The numerical simulation results are basically consistent with the measured data, then the proposed model is consistent with the matching states of permafrost moisture content and soil temperature in the region at different times. In addition, the experiments also demonstrate the reliability and accuracy of the model.

As the first practical representative technology in the new quantum revolution,quantum key distribution (QKD) brings a promising solution to achieve long-term security guarantee,which has made series of progress from research to practical deployment and applications in recent years.The architecture design of quantum secure communication network was firstly discussed,which extended point-to-point QKD link to multi-user QKD network.Furthermore,combining with the classical cryptography,an enhanced solution was proposed to apply the quantum key generated by QKD to the mobile terminals,which offered the possibility to apply QKD in general mobile applications.