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The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model's extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model's universal applicability. [ABSTRACT FROM AUTHOR]

This article introduces a dynamic average tracking algorithm for multiagent systems over weight-balanced direct graphs. Toward the end, we propose a proportional–integral distributive observer for each system with exponential convergence. In particular, the proposed algorithm only requires mild initialization conditions, does not rely on the time derivatives of reference signals and can ensure average consensus tracking with bounded steady-state error. The simulation results verified the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Memristive technologies are attractive due to their nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. For memristive devices, a model corresponding with practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. In this paper, we propose a novel memristor model based on the Ag/TiOx nanobelt/Ti configuration, which can reflect three different states (i.e. original stage, transition stage, and resistive switching state) of the physical memristor with a satisfactory fitting precision (greater than 99.88%). Meanwhile, this work gives (1) an insight onto the electrical characteristics of the memristor model under different humidity conditions; (2) the influence of the water molecular concentration on the memristor behavior, which is of importance for the memristor fabrication and subsequent applications. For verification purposes, the proposed three-state switchable memristor is applied into the memristor-based logic implementation. The experimental results demonstrate that the constructed circuit is able to realize basic Boolean logic operations with fast response speed and high efficiency. [ABSTRACT FROM AUTHOR]

• Sudden power imbalances bring system frequency and voltage issues. • Load participation has the potential to provide frequency/voltage support. • Network dynamics as optimization algorithms. • Performance of the method in the presence of high renewable penetration. Recently, more frequency and voltage stability issues have been caused by the uncertain load demand and intermittent renewable energy generation in smart grids. This has put significant pressure on turbine governor action. One potential solution is load participation in primary control. In this study, we investigate a general class of local load control (LLC) to provide grid frequency and voltage support while minimizing the pre-defined disutility to utilities and clients. Our approach relies only on local frequency and voltage measurements. We prove that the network dynamics under optimality conditions solve the dual problem of the disutility function adaptively, providing a principled way to guide forward engineering in local load control design. Simulation-based comparative study of choosing different types of disutility functions is conducted on response performance concerning frequency/voltage nadir and steady-state error. Moreover, the effectiveness and rationality of the proposed approach are verified in MATLAB/SIMULINK under various scenarios, including load variations, renewable power output fluctuations, and different penetrations of renewable energy. [ABSTRACT FROM AUTHOR]

Memristor is a novel passive electronic element with resistance-switching dynamics. Due to the threshold property and the variable conductivity of the memristive element, its composite circuits are promising for the implementation of logic operations. In this paper, a flexible logic circuit based on the threshold-type memristor and the mature complementary metal-oxide-semiconductor (CMOS) technology is designed for the realization of Boolean logic operations. Specifically, the proposed method is able to perform the NAND, AND, OR, and NOR gate operations through two phases, i.e. the writing operation and the reading operation. In such implementation, the total delay is very small especially for time-sequence inputs. Furthermore, for existing memristor-based logic implementation, a contrastive analysis with relevant computer simulations is carried out. The experimental results indicate that the proposed method is capable of realizing all basic Boolean logic operations, and some more complicated cascaded logic operations with more compact circuit structures, higher efficiency, and lower operating cost. [ABSTRACT FROM AUTHOR]

Mittag–Leffler stability is a property of fractional‐order dynamical systems, also called fractional Lyapunov stability, requiring the evolution of the positive‐definite functions to be Mittag–Leffler, rather than the exponential meaning in Lyapunov stability theory. Similarly, fractional Lyapunov function plays an important role in the study of Mittag–Leffler stability. The aim of this study is to create closed‐loop systems for commensurate fractional‐order non‐linear systems (FONSs) with Mittag–Leffler stability. We extend the classical backstepping to fractional‐order backstepping for stabilising (uncertain) FONSs. For this purpose, several conditions of control fractional Lyapunov functions for FONSs are investigated in terms of Mittag–Leffler stability. Within this framework, (uncertain) FONSs Mittag–Leffler stabilisation is solved via fractional‐order backstepping and the global convergence of closed‐loop systems is guaranteed. Finally, the efficiency and applicability of the proposed fractional‐order backstepping are demonstrated in several examples. [ABSTRACT FROM AUTHOR]

In this paper, the distributed optimization problems of multi-agent systems with directed communication topologies are investigated by using the game theory. In particular, a new general non-cooperative game model, termed state based weakly acyclic game, is provided to solve the problem. Based on this approach, the desired global objective is achieved by designing local objective function for individual agent to make coordination decisions. It is worth noting that all the obtained equilibria are thus solutions to the proposed distributed optimization problems with directed and time-varying communication topologies. Simulations on consensus problem in multi-agent systems are provided to verify the validness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Simple central force optimization (SCFO) algorithm is a novel physically-inspired optimization algorithm as simulating annealing (SA). To enhance the global search ability of SCFO and accelerate its convergence, a novel extended/enhanced central force optimization (ECFO) algorithm is proposed through both adding the historical information and defining an adaptive mass. SCFO and ECFO are all motivated by gravitational kinematics, in which the compound gravitation impels particles to the optima. The convergence of ECFO is proved based on a more complex characteristic equation than SCFO, i.e. the second order difference equation. The stability theory of discrete-time-linear system is used to analyze the motion equations of particles. Stability conditions limit their eigenvalues inside the unit cycle in complex plane and corresponding convergence conditions are deduced related with ECFO’s parameters. Finally, ECFO are tested against a suite of benchmark functions with deterministic and excellent results. Experiments results show that ECFO converges faster than SCFO with higher global searching ability. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the problem of multi-sensor optimal H ∞ fusion filtering for a class of discrete-time stochastic intelligent systems with missing measurements and time delays. This discrete-time intelligent system model, which is composed of a linear dynamic system and a bounded static nonlinear operator, presents a unified description of delayed or non-delayed intelligent systems composed of neural networks and Takagi and Sugeno (T–S) fuzzy models, Lur’e systems, and linear systems. The missing measurements from multi-sensors are described by a binary switching sequence that obeys a conditional probability distribution. We aim to design both centralized and distributed fusion filters such that, for all possible missing observations, the fusion error is globally asymptotically stable in the mean square, and the prescribed H ∞ performance constraint is satisfied. By employing the Lyapunov–Krasovskii functional method with the stochastic analysis approach, several delay-independent criteria, which are in the form of linear matrix inequalities (LMIs), are established to ensure the existence of the desired multi-sensor H ∞ fusion filters. An optimization problem is subsequently formulated by optimizing the H ∞ filtering performances, which is described as the eigenvalue problem (EVP). Finally, simulation examples are provided to illustrate the design procedure and expected performance. [Copyright &y& Elsevier]

The discrepancy in data distribution between training and testing scenarios, as well as the inductive bias of convolutional neural networks towards image styles, reduces the model's generalization ability. Many unsupervised domain generalization methods based on feature decoupling suffer from an initial neglect of explicit decoupling of content and style features, resulting in content features that still contain considerable redundant information, thereby restricting improvements in generalization capability. To tackle this problem, this paper optimizes the learning process of domain‐invariant (content) features into an information compression issue, minimizing redundancy in content features. Furthermore, to enhance decoupled learning, this paper introduces innovative cross‐domain loss functions and image reconstruction modules that explicitly decouple and merge content and style across different domains. Extensive experiments demonstrate the method's significant enhancements over recent cutting‐edge approaches. [ABSTRACT FROM AUTHOR]

Large-scale integration of PV generators in distribution grids will impair the voltage stability due to the stochastic and fluctuated PV power generation. To tame the volatile PV power generation, battery energy storage systems (BESS) are deployed as an effective yet expensive power buffering mechanism. In this paper, a dual ascent-based voltage optimization control is proposed to achieve the concurrent regulation of battery State-of-Charge (SoC), nodal voltages, and distribution loss. This control features the limited dependence on the communication network with information interaction between neighboring nodes. Besides, it can achieve the optimal power flow minimizing the distribution loss while maintaining the BESS SoC within a healthy range. The derivation of the control framework is provided, and comparative simulations in the IEEE 37-node distribution system are performed to validate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent. [ABSTRACT FROM AUTHOR]

This paper studied distributed estimation of the bounds on algebraic connectivity for a directed graph (i.e., digraph). As is well known, the main challenge of the underlying problem is how to enable local awareness of an entity otherwise prone to global information, in the presence of communication topology. More specifically, we introduce a novel state-dependent approach to estimate the bounds on algebraic connectivity with mild requirement on topology and communication effort. Compared with existing results, the proposed algorithm does not estimate eigenvalues or eigenvectors directly, rather it exploits their implications on the consensus procedure, and achieves a tradeoff between estimation accuracy and topological/communication requirement, and its convergence can be expected in a finite time. Simulation results verified the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

Pulse coupled neural network (PCNN) is a kind of visual cortex-inspired biological neural network, which has been proved a powerful candidate in the field of digital image processing due to its unique characteristics of global coupling and pulse synchronization. Notably, the inherent parameters estimation issue emerging in the entire system greatly affects the overall network performance. In this paper, a novel memristor crossbar array with its corresponding peripheral circuits is proposed, which is able to construct a general memristor-based PCNN (MPCNN) with variable linking coefficient. In order to verify the effectiveness and generality of the presented network, the single-channel MPCNN is further applied into the multi-focus image fusion problem with an improved multi-channel configuration. Correspondingly, a new type of MPCNN-based image fusion algorithm is put forward along with the design of an appropriate mapping function based on the image orientation information measure. Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms. [ABSTRACT FROM AUTHOR]

Here, a method for assessing the risk of bird pecking damage of composite insulators in ultra high voltage (UHV) lines is proposed using electric field (E‐field) simulation and deep learning. The distribution of E‐field on composite insulators is analysed via numerical simulation for different damage locations and damage sizes. Then, using the defective images of composite insulator strings captured in real inspection environments, the corresponding annotation image data set is constructed according to the finite element calculation results of E‐field under different damage conditions, and through the application of the YOLOX deep learning neural network, the risk assessment of the damage caused by bird pecking of insulators for UHV lines is conducted. Furthermore, to prevent overfitting of YOLOX with small‐scale images, transfer learning, as well as data enhancement, are applied to the YOLOX training process. According to the results, the mean average precision (mAP) of the model is 0.79, indicating that it is capable of high accuracy recognition, provides guidance for operation and maintenance personnel. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic approach to balance the active power between photovoltaic generators (PVs) and loads in autonomous microgrids. To achieve this, a distributed algorithm is designed to estimate the power difference between generation and consumption, then a finite-time consensus protocol is introduced to regulate the outputs of all the PVs in a cooperative and timely fashion, and the frequency deviation caused by active power unbalance can be compensated as well. In particular, the proposed distributed estimation and secondary control strategy is completely distributed and center-free in the sense that each PV and load are both self-organizing and global-awareness with only local communication, no centralized monitors are needed. Simulations on the standard IEEE 37-bus network are presented to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Data in real world are usually characterized in multiple views, including different types of features or different modalities. Multi-view learning has been popular in the past decades and achieved significant improvements. In this paper, we investigate three challenging problems in the field of incomplete multi-view representation learning, namely, i) how to reduce the influences produced by missing views in multi-view dataset, ii) how to learn a consistent and informative representation among different views and iii) how to alleviate the impacts of the inherent noise in multi-view data caused by high-dimensional features or varied quality for different data points. To address these challenges, we integrate these three tasks into a problem and propose a novel framework termed Noise-aware Incomplete Multi-view Learning Networks (NIM-Nets). NIM-Nets fully utilize incomplete data from different views to produce a multi-view shared representation which is consistent, informative and robust to noise. We model the inherent noise in data by defining the distribution $\Gamma $ and assuming that each observation in the incomplete dataset is sampled from the distribution $\Gamma $. To the best of our knowledge, this is the first work to unify learning the consistent and informative representation, alleviating the impacts of noise in data and handling the view-missing patterns in multi-view learning into a framework. We also first give a definition of robustness and completeness for incomplete multi-view representation learning. Based on NIM-Nets, we present joint optimization models for classification and clustering, respectively. Extensive experiments on different datasets demonstrate the effectiveness of our method over the existing work based on classification and clustering tasks in terms of different metrics. [ABSTRACT FROM AUTHOR]

With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies. [ABSTRACT FROM AUTHOR]

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. Ann-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws. [ABSTRACT FROM AUTHOR]

The attention mechanism of computer vision represented by a non-local network improves the performance of numerous vision tasks while bringing computational burden for deployment Wang et al. (2018). In this work, we explore to release the inference computation for non-local network by decoupling the training/inference procedure. Specifically, we propose the implicit non-local network (iNL). During training, iNL models the dependency between features across long-range affinities like original non-local blocks; during inference, iNL could be reformulated as only two convolution layers but can rival non-local network. In this way, the computation complexity and the memory costs are reduced. In addition, we take a further step and extend our iNL into a more generalized form, which covers the attentions of different orders in computer vision tasks. iNL brings steady improvements on multiple benchmarks of different vision tasks including classification, detection, and instance segmentation. In the meantime, it provides a brand–new perspective to understand the attention mechanism in deep neural networks. [ABSTRACT FROM AUTHOR]

Multi-scale features are essential for various vision tasks, such as classification, detection, and segmentation. Although Vision Transformers (ViTs) show remarkable success in capturing global features within an image, how to leverage multi-scale features in Transformers is not well explored. This paper proposes a scale-aware vision Transformer called ScopeViT that efficiently captures multi-granularity representations. Two novel attention with lightweight computation are introduced: Multi-Scale Self-Attention (MSSA) and Global-Scale Dilated Attention (GSDA). MSSA embeds visual tokens with different receptive fields into distinct attention heads, allowing the model to perceive various scales across the network. GSDA enhances model understanding of the global context through token-dilation operation, which reduces the number of tokens involved in attention computations. This dual attention method enables ScopeViT to "see" various scales throughout the entire network and effectively learn inter-object relationships, reducing heavy quadratic computational complexity. Extensive experiments demonstrate that ScopeViT achieves competitive complexity/accuracy trade-offs compared to existing networks across a wide range of visual tasks. On the ImageNet-1K dataset, ScopeViT achieves a top-1 accuracy of 81.1%, using only 7.4M parameters and 2.0G FLOPs. Our approach outperforms Swin (ViT-based) by 1.9% accuracy while saving 42% of the parameters, outperforms MobileViTv2 (Hybrid-based) with a 0.7% accuracy gain while using 50% of the computations, and also beats ConvNeXt V2 (ConvNet-based) by 0.8% with fewer parameters. • ScopeViT: a general vision transformer for multi-scale visual interactions. • Scale-aware attention module enriches understanding across spatial scales. • Extensive experiments show the superiority of ScopeViT on various visual tasks. [ABSTRACT FROM AUTHOR]

Multi-state series-parallel systems are widely-used for representing engineering systems. In real-life cases, engineers need to select an optimal system structure among many different multi-state series-parallel system structures. Screening of system structures is meaningful and critical. Moreover, to design a reliable structure, reliability evaluation is an indispensable part of the process. Due to the large number of available system structures, the computational burden can be huge when selecting the optimal one. Also, the number of components and possible states of each system can be enormous when the system scale is large, which causes significant complexity in exact reliability evaluation. To effectively select the optimal structure among numerous multi-state series-parallel systems under a reliability constraint, this article proposes an optimal structure screening method called the structure ordinal optimization. The proposed method combines the fuzzy universal generating function technique with an ordinal optimization algorithm. The fuzzy universal generating function technique is applied to reduce the computational time by approximately evaluating the reliability. Based on the approximate reliabilities, ordinal optimization helps to reduce the number of structure options and thus accelerate the screening process. Numerical examples show that the structure ordinal optimization method has advantages in computational efficiency with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Brain-inspired approaches can efficiently analyze the activities of biological neural networks and solve computationally hard problems with energy efficiencies unattainable with von Neumann architectures, indicating a significant improvement in the understanding of neuronal communications and functionalities. Here, we present a brain-inspired multimodal signal processing system with organic memristor arrays that can potentially integrate the signal sensory, storage, and computation. To facilitate the multimodal signal processing system design, we used four components. First, we present a multimodal signal sensory module mainly responsible for multimodal (iconic, echoic, olfactory, muscular, and gustatory) signal collection, fusion, and storage. Second, a high-density cross-point memristive synapse array is constructed after fabrication of the albumin protein memristor to realize the dense connectivity between layers of computing, data storage, and communication. Third, considering the structure and function of the brain region, we demonstrate a general learning module for hierarchy learning, which can recognize and imagine multimodal information. Finally, the necessary peripheral circuit module (consisting of winnerless competition function circuit, analog-to-digital converter, digital-to-analog converter, pulse modulator, etc.) is designed. Notably, our system can capture massive amounts of data every second and perform in situ processing of multimodal signals. This study is expected to help achieve the deep integration of nano materials into neuromorphic computing systems and energy-efficient integrated circuits. [ABSTRACT FROM AUTHOR]

An adaptive iterative learning control method is proposed for a class of nonlinear strict-feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. Ann-step ahead predictor approach is employed to estimate the future states in the control design. Discrete Nussbaum gain method is utilised to deal with the lack ofa prioriknowledge of control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis except for beginning states affected by random initial conditions. The effectiveness of the proposed control scheme is verified through numerical simulation. [ABSTRACT FROM AUTHOR]

The biology-inspired intelligent computing system for the neuromorphic hardware implementation is useful in high-speed parallel information processing. However, the traditional Von Neumann computer architecture and the unsatisfactory signal transmission approach have jointly limited the overall performance of the specific hardware implementation. In this paper, a compact extreme learning machine (ELM) architecture synthesized with the spintronic memristor-based synaptic circuit, the biasing circuit, and the activation function circuit is presented. Notably, due to the threshold characteristic of the memristive device, the synaptic circuit has a bimodal behavior. Namely, it is capable to provide the constant and adjustable network weights between the adjacent layers in the ELM. Furthermore, two major limitations (process variations and sneak path issue) are taken into account for the detailed robustness analysis of the whole network. Finally, the entire scheme is verified with case studies in single image super-resolution (SR) reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: Only two conditions were considered in paper titled as Constrained control of chaos appeared in [Y.-C. Tian, M.O. Tade, D. Levy, Phys. Lett. A 296 (2002) 87], in this Letter the third condition was toughed upon by their ways, and a special problem which was unlike the already results was investigated and the corresponding solving strategy was put forward, simulation results were also provided to verify the suggested scheme. [Copyright &y& Elsevier]

Abstract Traditionally, wind power and solar photovoltaic (PV) power generation is non-dispatchable and their normal operation relies on Maximum Power Point Tracking (MPPT) control. Therefore, it can be of highly disturbance to the system dispatch in particularly context of microgrids. To effectively fulfill dispatch command or market schedule, a novel cascading power sharing control (PSC) scheme is proposed to coordinate wind and solar PV power productions in microgrids while reducing the loss of renewable energy production involved. Considering different properties of wind and solar PV power generation systems, the discrepancies between dispatch command (market schedule) and actual renewable generation is firstly counterbalanced by adjusting wind power output via temperately storing or releasing kinetic energy of turbine rotors. Only when rotors of wind generator reach their limitations, should solar PVs begin to reduce their generation. The proposed PSC scheme is fully simulated in a microgrid with wind and solar PV, and the simulation results clearly indicate it can be more energy efficient than the traditional dispatch method while fulfilling the dispatch demand. Highlights • We focus on the power sharing control (PSC) in a microgrid with high penetration of renewables. • Energy production of wind generation (WG) and solar PV power generation is analyzed during PSC. • A novel cascading PSC scheme is proposed for fulfilling the dispatch order while reducing energy production loss. • The rotational mass of WG can be fully utilized for storing excessive energy and then released back when system needed. • The proposed scheme can be more energy-efficient than traditional one while fulfilling the dispatch demand. [ABSTRACT FROM AUTHOR]

This paper investigates the Nash equilibrium seeking problem among networked multi-player game over directed and switching topologies. To this end, a fully distributive gain adaptation strategy is proposed to make sure all players are updated without global information or initialization, and it is proved rigorously that the proposed scheme works well under a switching digraph and admits global convergence to the Nash equilibrium. [ABSTRACT FROM AUTHOR]