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Security management is the process of identifying a company's assets (such as people, buildings, equipment, systems, and information assets) and then developing, documenting, and implementing policies and procedures to secure those assets. Meanwhile, artificial intelligence (AI) applications are flourishing thanks to advances in deep learning and numerous hardware architecture improvements based on cloud edge computing (CEC) issues are associated with the Internet of Things (IoT), including inadequate security measures, user ignorance, and the dreaded active monitoring. Therefore, a 6G wireless communication‐assisted security management using artificial intelligence (WC‐SM‐AI) has been introduced to enhance security. The energy‐efficient 6G real‐time communication framework and the enhanced deep neural network security module are key components of this architecture. The first module optimizes network lifespan and spectral efficiency while reducing energy consumption and latency. Another module offers a more secure network connection while increasing privacy, data integrity, and access. For this reason, this article discusses how AI can strengthen the security of 6G networks while promoting strategy problems and solutions. [ABSTRACT FROM AUTHOR]

Industrial Automation (IA) and Artificial Intelligence (AI) need an integrated platform. Due to the uncertainty of the time required for training or reasoning tasks, it is difficult to ensure the real-time performance of AI in the factory. Thus in this paper, we carry out a detailed survey on cloud-edge computing-based Industrial Cyber Intelligent Control Operating System (ICICOS) for industrial automation and artificial intelligence. The ICICOS is built based on IEC61499 programming method and used to replace the obsolete Programmable Logic Controller (PLC). It is widely known that the third industrial revolution produced an important device: PLC. But the finite capability of PLC just only adapts automation which will not be able to support AI, especially deep learning algorithms. Edge computing promotes the expansion of distributed architecture to the Internet of Things (IoT), but little effect has been achieved in the territory of PLC. Therefore, ICICOS focuses on virtualization for IA and AI, so we introduce our ICICOS in this paper, and give the specific details. [ABSTRACT FROM AUTHOR]

Deep neural network is an important computer operating system in China. It has made great breakthroughs in target recognition, image classification and other fields. It plays a key role in multi-system integration and intelligent manufacturing industry, such as training and testing. The purpose of this paper is to study the multi-system fusion of deep neural network and its application in intelligent manufacturing industry. By setting up experiments, the multi-system fusion of neural network is carried out, combining with big data and artificial intelligence to verify the efficient operation of neural network in multi-system fusion. Using the methods of mathematical analysis and big data fitting, the collected data are classified, the experimental data are collected and then analyzed. In this paper, the reliability of this method is verified by research; the application of multi-system fusion based on deep neural network in intelligent manufacturing industry can effectively integrate all systems, improve the comprehensive working efficiency of each system by about 20% and improve the multi-system fusion of deep neural network and its application in intelligent manufacturing industry by about 15%. Thus, as the terminal of the system, the deep neural network plays the leading role of multiple systems. It can effectively integrate the various systems and improve the comprehensive work efficiency of each system, which has guiding significance for the development of industry. [ABSTRACT FROM AUTHOR]

Federated learning is a deep learning optimization method that can solve user privacy leakage, and it has positive significance in applying industrial equipment fault diagnosis. However, edge nodes in industrial scenarios are resource-constrained, and it is challenging to meet the computational and communication resource consumption during federated training. The heterogeneity and autonomy of edge nodes will also reduce the efficiency of synchronization optimization. This paper proposes an efficient asynchronous federated learning method to solve this problem. This method allows edge nodes to select part of the model from the cloud for asynchronous updates based on local data distribution, thereby reducing the amount of calculation and communication and improving the efficiency of federated learning. Compared with the original federated learning, this method can reduce the resource requirements at the edge, reduce communication, and improve the training speed in heterogeneous edge environments. This paper uses a heterogeneous edge computing environment composed of multiple computing platforms to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The heat generation is significant in the machining process, leading to thermal errors, and finally the geometric precision of machined parts is reduced. So the precision machine tool is a key factor in determining the geometric precision of complex parts. In recent years, the error control method is applied. But the method fails in reducing thermal errors because it cannot effectively process large-volume data, resulting from its low executing efficiency. To solve above issues, a new intelligent digital-twin prediction and reverse control system is designed for thermally induced errors based on the user-edge-cloud architecture to expedite the executing efficiency. The data-driven error modeling method is augmented by an error mechanism-based modeling to express the thermal error as a function with the temperature, armature current, rotational speed, and ambient temperature as independent variables, and then the long-term memorizing behavior of thermal errors is demonstrated. The error model is established based on an improved wavelet threshold denoising (IWTD) and a (long short-term memory) LSTM network to describe the memorizing behavior, and IWTD-LSTM network error prediction model is embedded into the digital-twin system. The digital-twin system and IWTD-LSTM network model were verified on a precision machine tool. With the implementation of the digital-twin system, the thermal error and the volume of the transferred data are reduced by 88.72% and 56.36%, respectively. [ABSTRACT FROM AUTHOR]

Fabric defect detection is the important step of ensuring the quality and price of textiles. In order to make the automatic fabric defect detection system used in production sites, a cloud–edge collaborative fabric defect system is proposed. Firstly, real-time defect detection is performed on edge device, the accuracy of small defect detection is ensured by improved MobileNetV2-SSDLite. The channel attention mechanism is introduced in the network to highlight defect features and suppress background noise features. The loss function is redefined by Focal Loss to overcome the imbalance of the number of defects and background candidate boxes. Then, detection result storage and model update are carried out in the cloud. Experiments show that the accuracy of the system is improved while maintaining the faster detection speed, among which, the accuracy of the Camouflage dataset with small defects has increased by 10.03% and the detection speed reaches 14.19FPS on NVIDIA Jeston Nano. • In the network improvement, the channel attention mechanism extract more refined defect features under the complex background disturbance, improving small defect detection accuracy. And Focal Loss solve the imbalance between defect and background default boxes number in the training process. • A lightweight network for defect detection in resource-constrained scenarios is proposed. Detection accuracy is improved for small defects and complex background datasets. Our method balances accuracy and inference time better, providing a new option for the embedded platform. • A cloud–edge collaborative fabric defect detection system is proposed. Combining edge devices and the cloud, the detection model is deployed on the edge device for real-time defect detection, data storage and model update at the cloud. [ABSTRACT FROM AUTHOR]

Cloud/edge computing and deep learning greatly improve performance of semantic understanding systems, where cloud/edge computing provides flexible, pervasive computation and storage capabilities to support variant applications, and deep learning models could comprehend text inputs by consuming computing and storage resource. Therefore, we propose to implement an intelligent online custom service system with power of both technologies. Essentially, task of semantic understanding consists of two subtasks, i.e., intent recognition and slot filling. To prevent error accumulation caused by modeling two subtasks independently, we propose to jointly model both subtasks in an end-to-end neural network. Specifically, the proposed method firstly extracts distinctive features with a dual structure to take full advantage of interactive and level information between two sub-tasks. Afterwards, we introduce attention scheme to enhance feature representation by involving sentence-level context information. With the support of cloud/edge computing infrastructure, we deploy the proposed network to work as an intelligent dialogue system for electrical customer service. During experiments, we test the proposed method and several comparative studies on public ATIS and our collected PSCF dataset. Experiment results prove the effectiveness of the proposed method by obtaining accurate and promising results. [ABSTRACT FROM AUTHOR]

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system's operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales. [ABSTRACT FROM AUTHOR]

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction. [ABSTRACT FROM AUTHOR]