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The development of offshore wind power is a crucial step in China’s energy transition and the achievement of carbon peaking and carbon neutrality goals. This paper first introduces a wireless communication technology for independent networking. Its applicability in the field of relay protection is analyzed, and a security encryption scheme for its use in relay protection services is designed. Considering the primary wiring form and protection function configuration of offshore wind power, a multi-terminal wireless differential protection method suitable for offshore wind power collection lines is proposed, achieving synchronization error less than 3° and typical delay of wireless self-organizing network communication less than 12ms. Utilizing multi-terminal data synchronization technology and differential protection algorithms, a scheme for fault isolation and recovery of non-faulty wind turbines is established, thereby achieving minimal range isolation of offshore wind power collection line faults and precise fault section location. The action time of multi-terminal differential protection is less than 40ms. Finally, experimental results confirm that this technology’s various metrics satisfy the demands of engineering applications and hold promotional value, offering a reference for the practical application of wireless communication technology in offshore wind power collection line protection services.

Simultaneous localization and mapping (SLAM) is considered as a key technique in augmented reality (AR), robotics and unmanned driving. In the field of SLAM, solutions based on monocular sensors have gradually become important due to their ability to recognize more environmental information with simple structures and low costs. Feature-based ORB-SLAM is popular in many applications, but it has many limitations in complex indoor scenes. Firstly, camera pose estimation based on monocular images is greatly affected by the environment; secondly, monocular images lack scale information and cannot be used to obtain image depth information; thirdly, monocular based SLAM builds a fused map of feature points that lacks semantic information, which is incomprehensible for machine. To solve the aforementioned issues, this paper proposes an SDF-SLAM model based on deep learning, which can perform camera pose estimation in a wider indoor environment and can also perform depth estimation and semantic segmentation on monocular images to obtain an understandable three-dimensional map. SDF-SLAM is tested and verified using a CPU platform and two sets of indoor scenes. The results show that the average accuracy of the predicted point cloud coordinates reaches 90%, and the average accuracy of the semantic labels reaches 67%. Moreover, compared with the state-of-the-art SLAM frameworks, such as ORB-SLAM, LSD-SLAM, and CNN-SLAM, the absolute error of the camera trajectory on indoor data with more feature points is reduced from 0.436 m, 0.495 m, and 0.243 m to 0.037 m, respectively. On indoor data with fewer feature points, they decrease from 1.826 m, 1.206 m, and 0.264 m to 0.124 m, respectively.

The task of Point-of-Interest (POI) recommendation is to recommend the next interest locations for users. Gated Graph Neural Network (GGNN) has been proved to be effective on POI recommendation tasks. However, existing GGNN solutions rarely consider the spatiotemporal information between nodes in the sequence graph, which is essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose an attention-based spatiotemporal gated graph neural network model (ATST-GGNN) for next POI recommendation. Firstly, the user’s check-in sequence is represented as a graph structure. Secondly, we use spatiotemporal context information to dynamically update nodes in the sequence graph, and obtain the complex transfer relationships between the check-ins. Thirdly, each session is then represented as the composition of the long and short preference using an attention network. However, current short preference fails to model union-level sequential patterns, we improve the local embedding representation of graph nodes by window pooling method, as well as the global embedding representation of graph nodes by integrating it into attention mechanism. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate and mean reciprocal ranking of ATST-GGNN method are greatly improved compared with the state-of-art methods. It has good application prospect.

In cross-cultural communication, multimedia animation is crucial in defining a nation’s image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national culture and highlight regional culture. Animation’s particular charm, position, and function in cultural dissemination are further highlighted by the special cinematic language used to portray emotions, and it also reflects the medium’s unique significance in the rapidly evolving modern society. To better understand the emotions generated by various multimedia animations, in- depth research is needed. To investigate these issues, this article explores the use of the Sigma-pi artificial neural network (SP-ANN) algorithm based on the grey wolf optimization algorithm (GWOA) to identify emotional states. Compared with traditional Sigma $\pi $ artificial neural network algorithms, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. Sigma $\pi $ networks can benefit from the proposed learning algorithms. This algorithm has high approximation accuracy and is particularly suitable for real-time approximation of nonlinear processes. The test results indicate that the proposed algorithm can work as expected.

Users often experience motion sickness symptoms after using Virtual Reality (VR) systems, which can jeopardize their health. If we can rapidly diagnose the vertigo levels of VR users and dynamically take anti-motion sickness measures based on the levels, such as adjusting the movement of objects in VR glasses, adding visual reference points, and changing image brightness, contrast, and refresh rate, we can quickly alleviate motion sickness symptoms. Therefore, rapid assessment of the VR vertigo levels becomes crucial. Deep learning methods can accurately diagnose vertigo levels, but due to the complex structure and high computational requirements of these deep models, they may not fully meet the need for speed. Additionally, these complex models may be challenging to implement in devices like VR glasses. Unlike deep neural networks, BP-MTN represents complex nonlinear functions as polynomial functions using a simple network structure based solely on addition and multiplication operations. This design significantly reduces model complexity. However, traditional MTN models are primarily used for prediction tasks and are not suitable for classification. To address this issue, this paper proposes a Backpropagation Multivariate Taylor Network (BP-MTN) classifier for diagnosing VR vertigo levels. Compared to the traditional MTN, the BP-MTN includes the following modifications: 1) adding fully connected layers to handle inconsistent input and output dimensions of the BP-MTN; 2) introducing a softmax layer after the traditional MTN’s output layer to enable classification; 3) incorporating activation functions after each output node to enhance the model’s ability for fitting nonlinearity. Experimental results demonstrate that the BP-MTN classifier achieves higher classification accuracy and faster speed compared to some deep learning models.

The stability of a grid-tied inverter system will be compromised in weak grid conditions. In order to address this problem, researchers have proposed using a power electronics transformer (PET) to connect inverters and the grid. By adjusting the controller parameters or control strategy, the output impedance of the PET low voltage (LV) converter can be varied to improve the system stability. This paper establishes impedance models of the PET-based grid-tied inverter system and evaluates the stability of the system using the generalized Nyquist criterion. The impact of the variety of control parameters on the system stability is then analyzed. To improve the stability of the system, two stabilized control strategies, namely virtual impedance and digital filter-based methods are proposed. Finally, the effectiveness of these strategies is evaluated through simulations and experiments.

The classic Elliptic curve digital Signature Algorithm (ECDSA) uses one inversion operation in the process of signature and verification, which greatly reduces the efficiency of digital signatures. Up to now, most research schemes improve efficiency by reducing reverse operations, but they fail to attach importance to such issues as forgery signature attack. At the same time, in the blockchain, the weak randomness of ECDSA will lead to the attack of forging random numbers, which is a potential problem of digital currency transactions. In consideration of this reason, in this article, an improved provably secure elliptic curve digital signature scheme is constructed. First, the new scheme introduces double parameters in the signature process, that can effectively resist the weak randomness attack of ECDSA in Bitcoin, and can be applied to blockchain digital currency trading systems. Second, in the random oracle model, it is provably indistinguishable against Elliptic Curve Discrete Logarithm Problem (ECDLP) under the super type I and type II adversary. Third, the new scheme avoids the inverse operation in the signature and verification phase. Compared with the ECDSA, the running speed is optimized by 50.1%. Similarly, the proposed scheme has higher computational efficiency than other existing algorithms.

Transient voltage peaking under very fast electrostatic discharge (ESD), like charged device model (CDM) pulse, is a serious problem to integrated circuits (ICs). A combined TCAD simulation and very fast transmission line pulse (VFTLP) testing method is proposed to thoroughly investigate the transient voltage peaking phenomena of diode-based ESD protection structures under CDM stressing. The study of a set of diode, diode-string and diode-triggered silicon-controlled rectifier (DTSCR) ESD protection structures, fabricated in a 28nm CMOS process, reveals that the inductive impedance along the ESD discharging path may be the root cause of voltage peaking under CDM stressing. The observation provides the design insights overcoming the voltage peaking problem in ESD protection designs before complicate CDM package level testing.

Cyber-Physical-Social Systems (CPSS) integrates cyber, physical and social spaces together, which makes our lives more convenient and intelligent by providing personalized service. In this paper, we will provide CPSS service for fine-grained recognition. Fine-grained visual recognition is a hot but challenging research in computer vision that aims to recognize object subcategories. The reason why it is challenging is that it extremely depends on the subtle discriminative features of local parts. Recently, some bilinear feature based methods were proposed, and the experimental results show state-of-the-art performance. However, most of them neglect the spatial relationships of part-region feature among multiple layers. In this paper, a novel approach of Self-layer and Cross-layer Bilinear Aggregation(SCBA) is proposed for fine-grained recognition. Firstly, a self-layer bilinear feature fusion module is proposed to model the spatial relationship of feature at the same layer. Secondly, we propose a cross-layer bilinear feature fusion module to capture the inter-layer interreaction of information to boost the ability of feature representation. In summary, the method we proposed not only can learn the correlations among different layers but the same layer, which makes it efficient and the experimental results show that it achieves state-of-the-art accuracy on three common fine-grained image datasets.

Perfluoroisobutyronitrile (C4F7N) has received extensive attention over the past three years as an environmental friendly gas insulating medium. At present, there are few studies on the compatibility of C4F7N with metal materials used in the electrical equipment. In this paper, we constructed a gas-solid interfacial reaction platform and carried out aging tests for C4F7N-CO2 gas mixture at different temperatures. The composition of the gas mixture and the surface morphology and the elemental composition of copper, aluminum were explored. The interaction mechanism between C4F7N-CO2 gas mixture and metal surface was also analyzed. It was found that C4F7N-CO2 gas mixture has better compatibility with aluminum than copper. The reaction between C4F7N-CO2 gas mixture and heated copper at 220 °C produces the gaseous by-product C3F6. The surface color of copper deepens and the corrosive degree increases with temperature, while the interaction between C4F7N and heated aluminum does not cause gas decomposition and metal corrosion. In general, the thermal stability of C4F7N-CO2 gas mixture associated with heated copper and aluminum is inferior to that of SF6.

As a new environmental-friendly gas insulating medium, perfluoroisobutyronitrile (C4F7N) has been widely concerned in recent years due to its relatively low global warming potential and excellent dielectric strength, which has the potential to replace the most greenhouse gas, sulfur hexafluoride(SF6). However, there are a few systematic studies on the influence of gas pressure and mixing ratio on the dielectric strength of C4F7N/CO2 gas mixture at present. In this paper, the power frequency breakdown characteristics of C4F7N/CO2 gas mixture under different pressure and mixing ratio conditions were tested using the gas insulation performance test platform. The optimal mixing ratio and pressure range of C4F7N/CO2 gas mixture for engineering application were also discussed. It is found that the breakdown voltages of C4F7N/CO2 gas mixture show a saturated growth trend with gas pressure and mixing ratio. The breakdown voltage of the gas mixture with 10% C4F7N can reach 80% of pure SF6 under the same condition. The insulation performance of the gas mixture with 20% C4F7N can reach more than 95% of pure SF6. Relevant results indicate that the gas mixture with 4%–12% C4F7N has the potential to be applied to high-voltage gas-insulated equipment.

The airdropping of multi-parafoil systems is of great significance to earthquake relief and military material transportation. In order to achieve the coordinated motion of multiple parafoils, a formation guidance strategy based on a virtual structure is proposed, which enables the formation of multiple parafoils to follow the planned trajectory and land at the target precisely. Firstly, since the main movement mode of a parafoil is turning and gliding, a multiphase homing trajectory for the reference point is planned, which mainly consists of a turning and gliding phase. Then, the trajectories of all the points on the virtual structure are generated by superimposing the relative positions of the virtual structure on the planned trajectory. Based on Lyapunov stability theory, a guidance strategy is designed to guide all parafoils to track the corresponding points on the virtual structure and complete the desired formation task. The simulation results show that the guidance strategy based on a virtual structure can effectively guide multiple parafoils to achieve coordinated formation movement. Parafoils dropped from different positions and heading angles can gradually gather together and form a formation, track the planned trajectories, land at the target point precisely and align up against the wind.

C5F10O (C5 perfluorinatedketone) has received extensive attention due to its great insulation and eco-friendly performance, which has the potential to replace SF6 usage in power industries. In this paper, the power-frequency breakdown properties and discharge decomposition characteristics of C5F10O/Air and C5F10O/N2 gas mixtures are explored. It is found that the AC breakdown voltage of the C5F10O gas mixtures increases gradually with both the gas pressure and the content of C5F10O. As the partial pressure of C5F10O increases, the relative insulation strength increasing trend of the C5F10O gas mixtures becomes less obvious with the increase in the gas pressure. The AC breakdown voltage of the C5F10O/Air gas mixture is higher than that of the C5F10O/N2 gas mixture. In addition, the decomposition products of the C5F10O gas mixtures are mainly CF4, C2F6, C3F8, C4F10, and C3F7H. Under the same condition, the content of the CF4 in C5F10O/Air gas mixture is higher than that of the C5F10O/N2 gas mixture. The decomposition of the C5F10O/Air gas mixture after breakdown did not produce C2F4 and C3F6. CF2O and CO2 are not detected in the C5F10O/N2 gas mixture. Moreover, the carbon precipitation on the electrodes' surface can be found for the C5F10O/N2 gas mixture after 60 breakdown tests.

The linear power flow (LPF) models are particularly important in the context of optimization algorithms for three-phase distribution systems with the high penetration of distributed renewable generations. With several approximations on network modelling, voltage drop calculation, link power calculation, voltage controlled bus and ZIP loads, this paper proposed a three-phase LPF model for three-phase unbalanced distribution networks based on loop-analysis theory. The method can deal with voltage-controlled (PV) & Load (PQ) buses and ZIP loads. The approximations of the star and delta connections ZIP loads are also analyzed. Test results based on several standard IEEE test feeders and an improved 615bus test system proved the effectiveness and accuracy of the proposed algorithm. The proposed LPF solution gives a simple, more robust, and potentially faster solution for modern distribution power system.

Underwater imaging has always been a challenge due to limitations imposed by scattering and absorption nature of the underwater environment. The light would be highly degraded after reflection and propagation in the water medium. Being an advanced imaging technique, Single-pixel Imaging (SPI) is applicable to acquire object spatial information in low light, severe backscattering, and high absorption conditions. Combination of Compressive Sensing (CS) and SPI can overcome the limitation of SPI algorithms such as long data-acquisition time, low reconstruction efficiency and poor reconstruction quality. In the current research, an underwater SPI system based on CS is established to reconstruct our two-dimensional (2D) transparent object. We have systematically investigated the influence of water turbid degree, measurement pattern types and number of measurements on image reconstruction performance. The proposed system is capable to reconstruct the object even when the turbidity reaches up to 80 Nephelometric Turbidity Unit (NTU), where the conventional imaging systems are unusable. Proposed reconstruction method in our research can save more than 70% data acquisition time, compared to SPI algorithm. Our experimental setup has been compared to a conventional imaging system and an underwater ghost imaging system to show its efficiency in obtaining accurate results from turbid water conditions. Furthermore, various algorithm comparison and imaging enhancement studies demonstrates that our algorithm is superior in bringing highly convex optimization at a faster rate with a smaller number of measurements. This work creates new insight into the SPI application and generates a guideline for researchers to improve their applications.

Multi-source remote sensing imagery has become widely accessible owing to the development of data acquisition systems. In this paper, we address the challenging task of the semantic segmentation of buildings via multi-source remote sensing imagery with different spatial resolutions. Unlike previous works that mainly focused on optimizing the segmentation model, which did not enable the severe problems caused by the unaligned resolution between the training and testing data to be fundamentally solved, we propose to integrate SR techniques with the existing framework to enhance the segmentation performance. The feasibility of the proposed method was evaluated by utilizing representative multi-source study materials: high-resolution (HR) aerial and low-resolution (LR) panchromatic satellite imagery as the training and testing data, respectively. Instead of directly conducting building segmentation from the LR imagery by using the model trained using the HR imagery, the deep learning-based super-resolution (SR) model was first adopted to super-resolved LR imagery into SR space, which could mitigate the influence of the difference in resolution between the training and testing data. The experimental results obtained from the test area in Tokyo, Japan, demonstrate that the proposed SR-integrated method significantly outperforms that without SR, improving the Jaccard index and kappa by approximately 19.01% and 19.10%, respectively. The results confirmed that the proposed method is a viable tool for building semantic segmentation, especially when the resolution is unaligned.

A low radar cross section (RCS) planar antenna using phase gradient metasurface is proposed in this paper. The proposed antenna works in the X-band and is fed by a parallel plate waveguide power divider. The designed phase gradient metasurface is coplanar with the feeding structure and couples guided waves in the parallel plate waveguide power divider to radiation waves. According to the generalized reflection law, the proposed metasurface is a frequency scanning antenna and the beam direction can be steered slightly from 16.6° to 13°. The proposed antenna is linearly polarized, and however, the RCS is reduced significantly for arbitrary polarized incident waves. The antenna was simulated, fabricated, and measured. The measurement results confirm well with the simulation results.

C6F12O has been introduced as a potential substitute to the most greenhouse gas SF6 used in the medium-voltage (MV) electrical equipment due to its great insulation and environmental performance. While there are few studies on the compatibility between C6F12O and metal materials used in the equipment which needs to be investigated and understood to avoid the negative impact on the material or gas. In this paper, we carried out experimental and theoretical studies on the interaction between C6F12O and copper, silver, aluminum. We found that the interaction between C6F12O and copper, aluminum introduces fluorine to the metal surface and generates some metal oxide. There was no significant change in the element composition and surface chemical states of silver after interacting with C6F12O. The interaction mechanism between C6F12O and copper, aluminum is attributed to the chemical adsorption and the interaction between C6F12O and silver belongs to the physical adsorption. The compatibility between C6F12O and silver is greater than that of copper, aluminum. Relevant results provide a reference for the design and manufacture of equipment using C6F12O gas mixture as the insulating medium.