Fault diagnosis method for spherical roller bearing of wind turbine based on variational mode decomposition and singular value decomposition

For the non-stationary characteristics of the vibration signal of wind turbine’s roller bearing in fault condition, a bearing fault diagnosis method based on variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed. The VMD method is used to decompose wind turbine’s roller bearing’s fault vibration signal into several components. These components are regard as initial feature vector matrix. The singular value decomposition of the matrix is done. The obtained singular value is used as the extracted bearing fault feature vectors. The probabilistic neural network is used as pattern recognition classifier to determine the working state and fault type of wind turbine roller bearings. The result of case study showed that the proposed method can effectively identify the working state and fault type of wind turbine roller bearings

Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.Comment: Accepted by IEEE Global Communications Conference (GLOBECOM) 202

Application of adaptive local iterative filtering and approximate entropy to vibration signal denoising of hydropower unit

In actual field testing environments of hydropower unit, unit vibration signals are often contaminated with noise. In order to obtain the real vibration signal, a vibration signal de-noising method of hydropower unit based on adaptive local iterative filtering (ALIF) and approximate entropy is presented. For the proposed method, the ALIF method is used to decompose vibration signal into several stable components. The approximate entropy of each component is calculated. According to a preset threshold value of approximate entropy, the eligible components are retained to achieve the noise cancellation of hydropower unit’s vibration signals. The ALIF-based method and the wavelet denoising method is compared by simulation signal and real signal. The root mean square error (RMSE), partial correlation index and signal to noise ratio (SNR) are used to evaluate the noise reduction ability of two methods. The results show that compared to the classical wavelet denoising method, the noise canceling ability of this proposed method has improved in some extent. It can more effectively suppress the noise of hydropower unit’s vibration signals. The denoised vibration signals are used to synthesize the shaft orbits of hydropower unit. This can effectively identify the rotor shaft orbit graphics and the operation state of hydropower unit

Integrated Sensing and Communications for IoT: Synergies with Key 6G Technology Enablers

The Internet of Things (IoT) and wireless generations have been evolving simultaneously for the past few decades. Built upon wireless communication and sensing technologies, IoT networks are usually evaluated based on metrics that measure the device ability to sense information and effectively share it with the network, which makes Integrated Sensing and Communication (ISAC) a pivotal candidate for the sixth-generation (6G) IoT standards. This paper reveals several innovative aspects of ISAC from an IoT perspective in 6G, empowering various modern IoT use cases and key technology enablers. Moreover, we address the challenges and future potential of ISAC-enabled IoT, including synergies with Reconfigurable Intelligent Surfaces (RIS), Artificial Intelligence (AI), and key updates of ISAC-IoT in 6G standardization. Furthermore, several evolutionary concepts are introduced to open future research in 6G ISAC-IoT, including the interplay with Non-Terrestrial Networks (NTN) and Orthogonal Time-Frequency Space (OTFS) modulation.Comment: 7 pages, 6 figure

Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardization and pre-standardization landscape

Identification of RoCYP01 (CYP716A155) enables construction of engineered yeast for high-yield production of betulinic acid

Betulinic acid (BA) and its derivatives possess potent pharmacological activity against cancer and HIV. As with many phytochemicals, access to BA is limited by the requirement for laborious extraction from plant biomass where it is found in low amounts. This might be alleviated by metabolically engineering production of BA into an industrially relevant microbe such as Saccharomyces cerevisiae (yeast), which requires complete elucidation of the corresponding biosynthetic pathway. However, while cytochrome P450 enzymes (CYPs) that can oxidize lupeol into BA have been previously identified from the CYP716A subfamily, these generally do not seem to be specific to such biosynthesis and, in any case, have not been shown to enable high-yielding metabolic engineering. Here RoCYP01 (CYP716A155) was identified from the BA-producing plant Rosmarinus officinalis (rosemary) and demonstrated to effectively convert lupeol into BA, with strong correlation of its expression and BA accumulation. This was further utilized to construct a yeast strain that yields \u3e 1 g/L of BA, providing a viable route for biotechnological production of this valuable triterpenoid

mIoT slice for 5G systems:Design and performance evaluation

Network slicing is a key feature of the upcoming 5G networks allowing the design and deployment of customized communication systems to integrate services provided by vertical industries. In this context, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet Quality of Service (QoS) constrained, mIoT services will need an ad-hoc end-to-end (E2E) slice, i.e., both access and core network with enhanced Control and User planes (CP/UP). After revising the key requirements of mIoT and identifying major shortcomings of previous generation networks, this paper presents and evaluates an E2E mIoT network slicing solution, featuring a new connectivity model overcoming the load limitations of legacy systems. Unique in its kind, this paper addresses mIoT requirements from an end-to-end perspective highlighting and solving, unlike most prior related work, the connectivity challenges posed to the core network. Results demonstrate that the proposed solution, reducing CP signaling and optimizing UP resource utilization, is a suitable candidate for next generation network standards to efficiently handle massive device deployment