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Purposes Deep learning based on centralized data can effectively improve the prediction accuracy of wind power, but the serious consequences of data leakage make wind farms constantly pay attention to the confidentiality of their own data, which hinders data-driven wind power prediction methods. Methods To solve the above problems, a short-term power prediction method was proposed for distributed multi-wind farms oriented to data privacy protection, which uses the Horizontal Federated Learning framework to complete the power prediction task of wind farms. First, wind farms adopt the distributed training method, and use the Temporal Pattern Attention (TPA) mechanism and Long Short-Term Memory (LSTM) network to form a TPA-LSTM local model to complete local data training. Then, the parameters of these local models are aggregated, and the weight values of the models are introduced to improve the contribution rate of the local models with good fitting effect. Finally, the update of global model parameters is implemented. Findings The results of experiments show that the global model obtained by this method has good prediction performance and generalization ability in multiple scenarios under the premise of ensuring the privacy of wind farm data.

Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-$\Sigma$) have demonstrated extraordinary performance in key technological areas, such as natural language processing and visual recognition, and have become the mainstream trend of artificial general intelligence. This has led more and more major technology giants to dedicate significant human and financial resources to actively develop their foundation model systems, which drives continuous growth of these models' parameters. As a result, the training and serving of these models have posed significant challenges, including substantial computing power, memory consumption, bandwidth demands, etc. Therefore, employing efficient training and serving strategies becomes particularly crucial. Many researchers have actively explored and proposed effective methods. So, a comprehensive survey of them is essential for system developers and researchers. This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage. Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems. Through comprehensive discussion and analysis, it hopes to provide a solid theoretical basis and practical guidance for future research and applications, promoting continuous innovation and development in foundation model systems.

HVDC transmission systems present a risk of commutation failure. For multi-infeed HVDC transmission systems, due to the interaction between AC and DC systems, the commutation failure of a single inverter station may cause multiple inverter stations to fail at the same time or in succession, which poses a threat to the safe and stable operation of the power grid. Based on the mechanism of commutation failure, the multi-infeed interaction factor was introduced to analyze the influencing factors and criteria of commutation failure in multi-infeed HVDC transmission systems, and the current methods and strategies for suppressing commutation failure were summarized. Finally, it pointed out that the mathematical model, the commutation failure coupling mechanism and the suppression measures were worthy of further discussion and research.

The Solid (Social Linked Data) project focuses on data sharing and privacy security and aims to build a decentralized ecosystem that radically changes the way web applications work today. Our goal is to introduce a “trust access authentication system” to achieve secure authentication and fine-grained access control, thereby promoting the implementation of Solid. Blockchain, equipped with multiple security properties and authentication functions, is a crucial technology. In this paper, we present a blockchain-assisted system for secure authentication in Solid and for implementation of fine-grained access control policies. Specifically, we explore to integrate threshold RSA signatures in a permissioned blockchain system to enable a fault-tolerant distributed signature scheme, thereby enhancing the resilience and robustness of authentication system. Moreover, we utilize smart contract to control transaction flows and manage access control policies automatically. Experimental results show that our proposed trust access authentication system enhances security, scales well, and is efficient and economically feasible.

Voltage source converter based high voltage direct current (VSC-HVDC) transmission systems has the advantages of good active and reactive power control capability and more suitable for multi-terminal transmission system. It is considered as a potential transmission mode. Research on control and stability of VSC-HVDC is an important factor affecting the safety and performance of transmission system. Aiming at the study of control strategies of VSC-HVDC systems, the topologies and decoupling and additional control methods of VSC-HVDC were analyzed. Then it extends from the basic two-terminal topologies to the multi-terminal and hybrid topologies, focus on the droop control, fault ride through capability of VSC-MTDC and control of hybrid direct current transmission system. Next, this paper discussed the stability and control schemes of VSC-HVDC for wind power integration, which will provide reference for future research.

The safety and stability of power systems is seriously threatened by subsynchronous oscillation (SSO) caused by integration of large-scale wind power. Due to the widespread adoption of power electronic devices, the mechanism and mode of the new type of SSO involving wind power are different from those of traditional SSO. Particularly, the SSO in doubly-fed induction generator (DFIG) based wind farms and direct-drive permanent magnet synchronous generator (PMSG) based wind farms have attracted extensive attention and research both at home and abroad. The commonly used analysis methods and their applicability were summarized. Then, the theoretical results and engineering practices in the aspects of analysis, control and protection of wind power SSOs in recent years were summarized based on typical engineering incidents. The paper aims to provides a reference for the future study of wind power SSO.

Networked robotics involves a collection of robots working together to perform complex tasks, such as search and rescue task in disaster management. Because such tasks are beyond the capacity of a single powerful robot, networked robotics has been widely researched. However, the modes of cooperation in traditional networked robotics have been restricted by the inherent physical constraint that all computations are performed in the robotic network, with knowledge sharing being limited to the collective storage in the network. Cloud robotics, which allows robots to benefit from the rich storage, computation, and communication resources of modern data centers, is widely accepted as a promising approach to efficient robot cooperation in applications, such as disaster management. In this paper, we study robotic cooperation in cloud robotics. We first give a conceptual view of the nature of this cooperation. We then propose three novel robotic cooperation frameworks for cloud robotics: robotic knowledge sharing cooperation, robotic physical-task cooperation, and robotic computation task cooperation. Finally, we identify several critical challenges, and illustrate the potential benefits of robotic cooperation in cloud robotics.

Transform learning has been proposed as a new and effective formulation for analysis dictionary learning, where the ℓ0 norm or the ℓ1 norm are generally used as sparsity constraint. The sparse solutions can be obtained by the hard thresholding or the soft thresholding. The hard thresholding is actually a greedy algorithm, which only obtains the approximate solutions; while the soft thresholding has a certain bias for the large elements. In this paper, we propose to employ the log regularizer instead of the ℓ0 norm and the ℓ1 norm in the overcomplete transform learning problem. Our minimization problem is nonconvex due to the log regularizer. We propose to employ a simple proximal alternating minimization method, where a closed-form solution of the log function could be obtained based on the proximal operator. Hence, an efficient and fast overcomplete transform learning algorithm is developed, which iterates based on the analysis coding stage and the transform update stage. The proposed algorithm can obtain sparser solutions and more accurate results from the theoretical analysis. Numerical experiments verify that the proposed algorithm outperforms existing transform learning approaches with the ℓ0 norm or the ℓ1 norm. Furthermore, the proposed algorithm is on par with the state-of-the-art image denoising algorithms.

Analysis sparse representation has recently emerged as an alternative approach to the synthesis sparse model. Most existing algorithms typically employ the l0-norm, which is generally NP-hard. Other existing algorithms employ the l1-norm to relax the l0-norm, which sometimes cannot promote adequate sparsity. Most of these existing algorithms focus on general signals and are not suitable for nonnegative signals. However, many signals are necessarily nonnegative such as spectral data. In this paper, we present a novel and efficient analysis dictionary learning algorithm for nonnegative signals with the determinant-type sparsity measure which is convex and differentiable. The analysis sparse representation can be cast in three subproblems, sparse coding, dictionary update, and signal update, because the determinant-type sparsity measure would result in a complex nonconvex optimization problem, which cannot be easily solved by standard convex optimization methods. Therefore, in the proposed algorithms, we use a difference of convex (DC) programming scheme for solving the nonconvex problem. According to our theoretical analysis and simulation study, the main advantage of the proposed algorithm is its greater dictionary learning efficiency, particularly compared with state-of-the-art algorithms. In addition, our proposed algorithm performs well in image denoising.

Rapid advances in the Internet-of-Things (IoT) have exposed the underlying hardware devices to security threats. As the major component of hardware devices, the integrated circuit (IC) chip also suffers the threat of illegal, malicious attacks. To protect against attacks and vulnerabilities of a chip, a credible authentication is of fundamental importance. In this paper, we propose a Hausdorff distance-based method to authenticate the identity of IC chips in IoT environments, where the structure is analyzed, and the lookup table (LUT) resources are treated as a set of reconfigurable nodes in field programmable gate array (FPGA)-based IC design. Unused LUT resources are selected for insertion of the copyright information by using the depth-first search algorithm, and the random positions are reordered with the Hausdorff distance matching function next, so these positions are mapped to satisfy the specific constraints of the optimal watermark positions. If the authentication process is activated, virtual positions are mapped to the initial key file, yet the identity of the IC designed can be authenticated using the mapping relationship of the Hausdorff distance function. Experimental results show that the proposed method achieves good randomness and secrecy in watermark embedding, as well the extra resource overhead caused by watermarks are promising.

The impact of Type 4 wind turbine generator (WTG)-based 10 million megawatt clusters (TMMC) on small-signal dynamics of power systems was investigated using the second-generation generic models (GM) of Western Electricity Coordinating Council (WECC). A WTG participation index (WTG PI) was defined to investigate the impact of Type 4 WTGs on the traditional interarea electromechanical modes. To identify the new electromechanical modes dominated by Type 4 WTGs, an identification factor (IF) was also defined using participation factors. Given the increasing penetration of Type 4 WTGs replacing synchronous generators, the changed law of damping and frequencies of the traditional interarea modes was also investigated using the WTG PI. One new type of electromechanical mode dominated by Type 4 WTGs was identified by using the defined IF. These new modes can be divided into two categories: strong-interaction modes and weak-interaction modes, depending on the number of participating WTGs. The strong-interaction modes dominated by Type 4 WTGs can result in widely spread power oscillations in power systems. The results of small-signal analysis were validated by time domain simulation and mode detection.