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A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.

The connectivity of a network is an important indicator for assessing its reliability and fault-tolerability. In this paper, we study a novel measurement, which is the h -faulty-block connectivity. Given a connected graph G and a nonnegative integer h , let C ⊂ V ( G ) and G [ C ] be a connected subgraph. Then, C is called an h -faulty-block of G if G - C is disconnected, and every remaining component of G - C has at least h + 1 nodes. The minimum cardinality over all h -faulty-blocks of G is called h -faulty-block connectivity of G , denoted by F B k h ( G ) . In this paper, we focus on the alternating group graphs and split-star networks. We study the { 0 , 1 , 2 } -faulty-block connectivity of the two kinds of graphs and show that F B k 0 ( A G n ) = 3 n - 7 for n ≥ 4 , F B k 1 ( A G n ) = 5 n - 14 for n ≥ 5 , F B k 2 ( A G n ) = 7 n - 22 for n ≥ 6 , and F B k 0 ( S n 2 ) = 3 n - 5 for n ≥ 4 , F B k 1 ( S n 2 ) = 5 n - 11 for n ≥ 5 , F B k 2 ( S n 2 ) = 7 n - 18 for n ≥ 6 .

Let H and M be two connected subgraphs of an interconnection network G . If the removal of any minimum H -structure-cut (resp. H -substructure-cut) isolates a component isomorphic to M , then G is said to be super H | M -connected (resp. super sub - H | M - connected ). Furthermore, if the removal of any minimum H -structure-cut (resp. minimum H -substructure-cut) splits G into exactly two components, one of which is isomorphic to M , then G is said to be hyper H | M -connected (resp. hyper sub - H | M - connected ). The n -dimensional star network S n is one of alternative interconnection networks for multiprocessor systems. Let n ≥ 4 . In this paper, we prove that S n is hyper H | K 1 -connected and hyper sub- H | K 1 -connected for H ∈ { K 1 , r , P 5 } and 1 ≤ r ≤ n - 3 , S n is hyper P 4 | K 1 , 1 -connected and hyper sub- P 4 | K 1 , 1 -connected, S n with n ≥ 5 is super C 6 | K 1 -connected, and S n is hyper sub- C 6 | K 1 -connected, where K 1 is the complete graph on one vertex and K 1 , r is a star on 1 + r vertices, P k is a path on k vertices and C 6 is a cycle on 6 vertices.

We investigate the emergence of amplitude and frequency chimera states in ring–star networks consisting of identical Chua circuits connected via nonlocal diffusive, bidirectional coupling. We first identify single-well chimera patterns in a ring network under nonlocal coupling schemes. When a central node is added to the network, forming a ring–star network, the central node acts as the distributor of information, increasing the chances of synchronization. Numerical simulations show that the radial coupling strength k between the central and the peripheral nodes acts as an order parameter leading from a lower- to a higher-frequency domain. The transition between the domains takes place for intermediate coupling values, 0.5 < k < 2 , where the frequency chimera states prevail. The transition region (width and boundaries) depends on the Chua oscillator parameters and the network specifics. Potential applications of star connectivity can be found in the control of Chua networks and in other coupled chaotic dynamical systems. By adding one central node and without further modifications to the individual network parameters, it is possible to entrain the system to lower- or higher-frequency domains as desired by the particular applications.

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and “rank-order” coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays.

Satellite communication networks have gradually been recognized as an effective way to enhance the ground-based wireless communication. Considering the weight restriction of payloads, multi-antenna technologies have recently come into use on satellite platforms, and are capable of generating beams flexibly to provide services. To avoid incurring interferences, adjacent beams are designed to take different spectral resources. Unfortunately, this may limit the simultaneously accessed terminals since the spectrum cannot be fully used. In this paper, we propose a spectrum-saving transmission method in a satellite star network, where terminals communicate with each other through the hub station. Taking advantage of the great transmission capability differences of the hub station and terminals, we could allocate them the same spectral resources. Specifically, it is not necessary to use exclusive frequency bands for terminals.The proposed method can play a significant role when large numbers of users need to access the system with limited spectrum resource. To give a deep insight into the spectrum-saving method, the expressions of ergodic sum-rate are provided, and the impact of the number of accessed terminals is further analyzed. Simulation results validate the advantage of the proposed method in terms of bit error rate and ergodic sum rate.

In this paper, network dynamics are investigated in a periodically forced chemical system. At the same time, the ring network and ring-star network based on the periodically forced chemical system are designed. The chaotic dynamics of the ring network and ring-star network are analyzed by using the Lyapunov exponent spectrum, bifurcation diagram and correlation function. We show that the coupling strength of ring network has an important influence on chaotic dynamics and synchronization. By comparing ten, eleven and 100 nodes, we find that the bifurcation path of the ring-star network is robust to the number of nodes, which is different from the ring network. In addition, the ring-star network in comparison with the ring network achieved chaotic complete synchronization among all nodes. Finally, we proposed a new chaotic whale optimization (CWO) algorithm using the randomness of the ring-star network. It is used to tune the parameters of the PID controller with large time-delay systems. The simulation results show that the proposed CWO algorithm presents better performance than other available algorithms in the literature.

The connectivity is an important measurement for the fault-tolerance of a network. The generalized connectivity is a natural generalization of the classical connectivity. An -tree of a connected graph is a tree that contains all the vertices in subject to . Two -trees and are internally disjoint if and only if and . Denote by the maximum number of internally disjoint -trees in graph . The generalized -connectivity is defined as . Clearly, . In this article, we show that , where is the hierarchical star network.

IntroductionUnderwater image quality is commonly affected by problems such as insufficient illumination, extensive background noise, and target occlusion. Conventional biological detection methods suffer from the limitations of weak feature extraction, high computation, and low detection efficiency.MethodsWe propose an efficient and lightweight YOLO network for robots to realize high-precision underwater biological detection. Firstly, a backbone network based on hybrid dilated attention (HDA) is designed to expand the receptive field and focus on key features effectively. Secondly, a mixed aggregation star (MAS) network for the neck is constructed to enhance complex structural features and detailed textures of underwater organisms. Finally, the detection head is lightweighted using multi-scale content enhancement (MCE) modules to adaptively enhance key target channel information and suppress underwater noise.ResultsCompared to state-of-the-art target detection algorithms in underwater robots, our method achieves 85.7.% and 87.9% mAP@0.5 on the URPC2021 and the DUO datasets, respectively, with a model size of 5.19 M, a FLOP of 6.3 G, and a FPS of 16.54.DiscussionThe proposed method has excellent detection performance in underwater environments with low light, turbid water, and target occlusion.

Networks pervade social and economic life, and they play a prominent role in explaining a huge variety of social and economic phenomena. Standard economic theory did not give much credit to the role of networks until the early 1990s, but since then the study of the theory of networks has blossomed. At the heart of this research is the idea that the pattern of connections between individual rational agents shapes their actions and determines their rewards. The importance of connections has in turn motivated the study of the very processes by which networks are formed.