Random Topologies Research Articles

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge.

Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

C-Terminal p53 Palindromic Tetrapeptide Restores Full Apoptotic Function to Mutant p53 Cancer Cells In Vitro and In Vivo.

We previously demonstrated that a synthetic monomer peptide derived from the C-terminus of p53 (aa 361−382) induced preferential apoptosis in mutant p53 malignant cells, but not normal cells. The major problem with the peptide was its short half-life (half-life < 10 min.) due to a random coil topology found in 3D proton NMR spectroscopy studies. To induce secondary/tertiary structures to produce more stability, we developed a peptide modelled after the tetrameric structure of p53 essential for activation of target genes. Starting with the above monomer peptide (aa 361−382), we added the nuclear localization sequence of p53 (aa 353−360) and the end of the C-terminal sequence (aa 383−393), resulting in a monomer spanning aa 353−393. Four monomers were linked by glycine to maximize flexibility and in a palindromic order that mimics p53 tetramer formation with four orthogonal alpha helices, which is required for p53 transactivation of target genes. This is now known as the 4 repeat-palindromic-p53 peptide or (4R-Pal-p53p). We explored two methods for testing the activity of the palindromic tetrapeptide: (1) exogenous peptide with a truncated antennapedia carrier (Ant) and (2) a doxycycline (Dox) inducer for endogenous expression. The exogenous peptide, 4R-Pal-p53p-Ant, contained a His tag at the N-terminal and a truncated 17aa Ant at the C-terminal. Exposure of human breast cancer MB-468 cells and human skin squamous cell cancer cells (both with mutant p53, 273 Arg->His) with purified peptide at 7 µM and 15 µM produced 52% and 75%, cell death, respectively. Comparatively, the monomeric p53 C-terminal peptide-Ant (aa 361−382, termed p53p-Ant), at 15 µM and 30 µM induced 15% and 24% cell death, respectively. Compared to the p53p-Ant, the exogenous 4R-pal-p53p-Ant was over five-fold more potent for inducing apoptosis at an equimolar concentration (15 µM). Endogenous 4R-Pal-p53p expression (without Ant), induced by Dox, resulted in 43% cell death in an engineered MB468 breast cancer stable cell line, while endogenous p53 C-terminal monomeric peptide expression produced no cell death due to rapid peptide degradation. The mechanism of apoptosis from 4R-Pal-p53p involved the extrinsic and intrinsic pathways (FAS, caspase-8, Bax, PUMA) for apoptosis, as well as increasing reactive oxygen species (ROS). All three death pathways were induced from transcriptional/translational activation of pro-apoptotic genes. Additionally, mRNA of p53 target genes (Bax and Fas) increased 14-fold and 18-fold, respectively, implying that the 4R-Pal-p53p restored full apoptotic potential to mutant p53. Monomeric p53p only increased Fas expression without a transcriptional or translational increase in Fas, and other genes and human marrow stem cell studies revealed no toxicity to normal stem cells for granulocytes, erythrocytes, monocytes, and macrophages (CFU-GEMM). Additionally, the peptide specifically targeted pre-malignant and malignant cells with mutant p53 and was not toxic to normal cells with basal levels of WT p53.

Distributed Rendezvous placement for producer mobility support in NDN-IoT

Named Data Networking is a new networking architecture proposed as a potential design for the next generation of networks. However, supporting the mobility of producers in NDN-based IoT systems remains an open challenge for researchers to address. To tackle this problem, the NDN research group introduced a solution called KITE [1]. Since this solution is an initial design, some issues to be resolved remain, including the rendezvous placement selection. The location of the rendezvous servers can significantly reduce the path stretch efficiently. This paper focuses on improving the KITE solution by addressing two specific issues. First, we aim to determine the optimal number of rendezvous servers to use in the network. Second, we seek to identify the best locations for the rendezvous servers to minimize the path stretch efficiently. We propose an evaluation metric called average hop count based on network centrality measures to select the number and the location for the rendezvous. We evaluate the Average hop count under different network centrality measures using random scale-free network topology. Our results show that the betweenness centrality measure performs better than other measures, and we, therefore, suggest using it as a metric for selecting the number of rendezvous servers and their locations.

Accelerating Parallel Applications Based on Graph Reordering for Random Network Topologies

The Message Passing Interface (MPI) is a crucial programming tool for enabling communication between processes in parallel applications. The goal of MPI users is to allocate tasks to processors in a way that maximizes both spatial and temporal locality in the network. However, this can be challenging, especially in large-scale networks where maximizing processor locality may not be feasible at runtime. To address this issue, we propose the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamorder</i> , an offline node reassignment approach that takes into account physical processor locations based on graph reordering for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Random</i> network topologies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamorder</i> aims to optimize task mapping for improved performance in parallel applications, whether for multiple tasks or within a single task. Additionally, we investigate the potential of improving MPI applications through runtime parameter tuning based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamorder</i> . Our evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamorder</i> provides a 27.3% improvement in performance compared to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gorder</i> algorithm on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Random</i> topologies, which is a state-of-the-art solution designed with the aim of enhancing cache locality and achieves this goal by rearranging the vertices of a graph in a way that places the vertices that are typically accessed together in close proximity. Moreover, our autotuning framework using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamorder</i> results in an average speedup of 1.38x for targeted MPI applications by searching through various runtime parameter combinations.

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and polling applications. The distribution portion of the power delivery network, which we focus on in this work, is the most topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) based solely on topology descriptors.We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.

Adaptive quantitative control for finite time synchronization among multiplex switched nonlinear coupling complex networks

Synchronization is an essential factor of multiplex dynamic networks. We study synchronization among multiplex delay networks with random switching nonlinear coupling under random disturbances. Different from the existing research works, this paper focuses on multiplex delay nonlinear coupling networks with Bernoulli random switching topologies. To overcome the time-varying dynamic characteristics of the multiplex networks and describe the limitation in signal transmission process, an adaptive delay-dependent quantitative tracking control policy is presented. To achieve synchronization among the multiplex switched delay networks in a limited time, finite time control technology is introduced into the design of the adaptive quantitative controller in the policy. The theoretical framework about the stochastic synchronization among the multiplex switched nonlinear coupling networks is derived. We find that the setting time is related to quantization level, parameters of controllers and Lyapunov functions. Finally, an example is presented to show the effective for the theoretical framework.

Efficient practical Byzantine consensus using random linear network coding

Using random linear network coding (RLNC) in asynchronous networks with one-to-many information flow has already been proven to be a valid approach to maximize the channel capacities. Message-based consensus protocols such as practical Byzantine fault tolerance (pBFT) adhere partially to said scenario. Protocol phases with many-to-many communication, however, still suffer from quadratic growth in the number of required transmissions to reach consensus. We show that an enhancement in the data transmission behavior in the quadratic phases is possible through combining RLNC with pBFT as one hybrid protocol. We present several experiments conducted on random network topologies. We conclude that using RLNC-based data transmission offers a significantly better performance under specific circumstances, which depend on the number of participating network nodes and the chosen coding parameters. Applying the same approach to other combinations of message-based consensus and network coding protocols promises not only a gain in performance, but may also improve robustness and security and open up new application scenarios for RLNC, e.g., running it on the application layer.

Crossover‐SGD: A gossip‐based communication in distributed deep learning for alleviating large mini‐batch problem and enhancing scalability

SummaryDistributed deep learning is an effective way to reduce the training time for large datasets as well as complex models. However, the limited scalability caused by network‐overheads makes it difficult to synchronize the parameters of all workers and gossip‐based methods that demonstrate stable scalability regardless of the number of workers have been proposed. However, to use gossip‐based methods in general cases, the validation accuracy for a large mini‐batch needs to be verified. For this, we first empirically study the characteristics of gossip methods in a large mini‐batch problem and observe that gossip methods preserve higher validation accuracy than AllReduce‐SGD (stochastic gradient descent) when the number of batch sizes is increased, and the number of workers is fixed. However, the delayed parameter propagation of the gossip‐based models decreases validation accuracy in large node scales. To cope with this problem, we propose Crossover‐SGD that alleviates the delay propagation of weight parameters via segment‐wise communication and random network topology with fair peer selection. We also adapt hierarchical communication to limit the number of workers in gossip‐based communication methods. To validate the effectiveness of our method, we conduct empirical experiments and observe that our Crossover‐SGD shows higher node scalability than stochastic gradient push.

Design criteria for enhanced energy constraint MAC protocol for WSN

There are many different ways to describe a network. There are many tiny, light-weight wireless sensor nodes scattered throughout the environment to monitor and manage the environment. A sensor network's most fundamental component is the sensor node. Sensors, CPUs, a transceiver, memory, and a power supply are all on board. There are several devices in a wireless sensor network (WSN). The most important WSN criteria are network longevity, data collection, and security. " Wireless sensors' low cost and ease of deployment make them ideal for a wide range of real-time applications. Many MAC-layer protocols have been devised and are now being tested in an effort to limit the number of depletion methods. S-MAC protocol is the primary focus of our endeavour to implement the suggested model that supports the unique characteristics of optimization of synchronisation cycle. For example, Backoff Exponent is one of the strategies that are used to prevent collisions. Deriving the advantages of a successful communication system is made possible by our approach to node-to-node coordination. All nodes within the carrier sensing range are considered in our suggested scheme's random deployment topology.

Consensus Tracking of Nonlinear Agents Using Distributed Nonlinear Dynamic Inversion with Switching Leader-Follower Connection

In this paper, a consensus tracking protocol for nonlinear agents is presented, which is based on the Nonlinear Dynamic Inversion (NDI) technique. Implementation of such a technique is new in the context of the consensus tracking problem. The tracking capability of nonlinear dynamic inversion (NDI) is exploited for a leader-follower multi-agent scenario. We have provided all the mathematical details to establish its theoretical foundation. Additionally, a convergence study is provided to show the efficiency of the proposed controller. The performance of the proposed controller is evaluated in the presence of both (a) random switching topology among the agents and (b) random switching of leader-follower connections, which is realistic and not reported in the literature. The follower agents track various trajectories generated by a dynamic leader, which describes the tracking capability of the proposed controller. The results obtained from the simulation study show how efficiently this controller can handle the switching topology and switching leader-follower connections.

The distributions under two species-tree models of the number of root ancestral configurations for matching gene trees and species trees

For a pair consisting of a gene tree and a species tree, the ancestral configurations at a species-tree internal node are the distinct sets of gene lineages that can be present at that node. The enumeration of root ancestral configurations—ancestral configurations at the species-tree root—assists in describing the complexity of gene-tree probability calculations in evolutionary biology. Assuming that the gene tree and species tree match in topology, we study the distribution of the number of root ancestral configurations of a random labeled tree topology under the uniform and Yule–Harding models. We employ analytic combinatorics, considering ancestral configurations in the context of additive tree parameters and using singularity analysis to evaluate asymptotic growth of the coefficients of generating functions. For both models, we obtain asymptotic lognormal distributions for the number of root ancestral configurations. For Yule–Harding random trees, we also obtain the asymptotic mean (∼1.425n) and variance (∼2.045n) of the number of root ancestral configurations, paralleling previous results for the uniform model (mean (4/3)n, variance ∼1.822n). A methodological innovation is that to obtain the Yule–Harding asymptotic variance, singularity analysis is conducted from the Riccati differential equation that the generating function satisfies—without possessing the generating function itself.

In-situ investigation on the microstructure evolution of Mg-2Gd alloys during the V-bending tests

In this work, the in-situ observation of the microstructure in the Mg-2Gd (wt%) alloys during V-bending tests was operated. The microstructure and texture evolutions were characterized by the in-situ electron backscatter diffraction (EBSD), scanning electron microscopy (SEM) and the high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) technique. The results revealed a unique microcracks nucleation mechanism in the Mg-Gd alloys compared with the traditional Mg alloy (AZ31 alloy). The microcrack was nucleated at the grain boundary for the Mg-2Gd alloy and in the intragranular for the AZ31 alloy during the bending process. This difference mode between the Mg-Gd and the AZ31 alloys was mainly attributed to the more random topology of the grain boundary network, the lower grain boundary cohesion and the enhanced hinder ability for the dislocations due to the segregated Gd atoms in the Mg-Gd alloy. Furthermore, the microcracks that had a large angle θ with the extruded direction (ED) in the Mg-Gd alloys were preferred nucleated since grain boundaries had the larger normal stress during the bending process.

A brain-inspired algorithm for training highly sparse neural networks

Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient. Pruning the dense neural networks is among the most widely used methods to obtain a sparse neural network. Driven by the high training cost of such methods that can be unaffordable for a low-resource device, training sparse neural networks sparsely from scratch has recently gained attention. However, existing sparse training algorithms suffer from various issues, including poor performance in high sparsity scenarios, computing dense gradient information during training, or pure random topology search. In this paper, inspired by the evolution of the biological brain and the Hebbian learning theory, we present a new sparse training approach that evolves sparse neural networks according to the behavior of neurons in the network. Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, “Cosine similarity-based and random topology exploration (CTRE)”, evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward. We carried out different experiments on eight datasets, including tabular, image, and text datasets, and demonstrate that our proposed method outperforms several state-of-the-art sparse training algorithms in extremely sparse neural networks by a large gap. The implementation code is available on Github.

Dynamic Event-Triggered Platooning Control of Automated Vehicles Under Random Communication Topologies and Various Spacing Policies.

This article addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.

A comprehensive multi-topology minimum set cover link-state routing approach for emerging random all-IP access network topologies

Wireless access networks are ever-changing their setup, topologies and provisions of coverage. Forthcoming deployment trends include densification of cells and convergence of the ‘native IP’ with cellular access networks with more than best-effort expectations. Greater meshing in networks due to practical deployment requirements is inevitable and needed. This paper proposes a routing protocol to address the “randomness” of topology interconnections, routing paths and sizes in emerging IP access networks. Termed as Minimum Set Cover (MSC) approach, it is a generalization of the NP and NP complete mathematical problem. The offline component of the MSC multiplies intra-domain routing installations, called Routing Planes (RPs) modelled as graphs, to suitably cover the whole routing topology prior to the traffic injections in the online component. We introduce novel offline optimization features using a dynamic cost function that plans availability of capacities and correlation of routing paths via the chosen set of RPs. Our simulations verify effective path diversity using MSC with its modest protocol overhead and a heuristic used for RPs selection. For randomly constructed access network topologies with 7, 18 and 33 routers and various meshing levels, our results show convincing suitability and key performances gains compared with rival routing solutions.

Progress on research of construction of carbon nanotubes/carbon fiber cross-scale interface enhancement systems

<p indent="0mm">Carbon fiber reinforced composites (CFRP) have a broad application prospect in aerospace, military equipment and other fields because of their many excellent properties such as high strength and stability. However, the inert surface structure of carbon fiber (CF) seriously affects the load transfer efficiency with the matrix, which hinders the full play of the excellent performance of the composites. Therefore, the optimization and design of the interfacial structure between the two phases has become a hot research topic. Carbon nanotubes (CNTs) with excellent mechanical properties were deposited on the surface of CF to form a three-dimensional network hybrid structure with multiple orders of magnitude, thereby improving the interfacial bonding ability between the CF and the matrix. This paper introduces four process routes for the construction of cross-scale enhancement systems, including chemical vapor deposition (CVD), electrophoretic deposition (EPD), chemical grafting and dip-coating spraying process. The advantages, limitations and key process parameters of each method are summarized. The study of CVD method focuses on the influence of process parameters such as catalysts (type, morphology, concentration), growth time, treatment temperature, carbon source and carrier gas (type, mixing ratio, inlet speed) on the growth morphology (length, density, thickness) of CNTs. This method achieves the direct directional growth of CNTs on the CF surface, but the CF is affected by the pre-activation treatment, metal catalyst etching, and high-temperature environment, which leads to a significant decrease in its own strength. In the EPD method, the adjustment of key process parameters (applied voltage, energization time, electrode distance, and electrode shape) becomes the key to successful application of the EPD method. Although the EPD method is capable of producing textured CNT coatings or thin films on the CF, the weak van der Waals force bonding form and the “disorder” of deposition are the main pain points of this method. In the chemical grafting process, the reaction between multiple reactive functional groups and the use of macromolecular coupling agents provide a more diversified preparation scheme, and the bonding strength between CNTs and CF is greatly enhanced by chemical bonding. The construction of CNTs/CF interfacially enhanced structures, using two different processes, surface dip coating and spraying, is considered to be simple and effective, and the degree of dispersion of CNTs in sizing agents or different solvents becomes an important consideration for good deposition on the CF surface. Although the combination between CNTs and CF is not strong, the simple operation process and reasonable economic cost show a high suitability for industrialization. In this paper, three different attachment types of CNTs, namely radial embedding, random topology and surface attachment, are proposed in summary to investigate the stress transfer paths between the composite matrix and the form of interfacial enhancement. The three different attachment types contribute to the enhancement of the interface properties in different ways and to different degrees, with radially embedded structures contributing the most to the enhancement of the interface properties, vertically oriented CNTs significantly weakening the radial stresses between the CF and the matrix, and surface-applied CNTs suppressing the shear stresses more significantly than the radial stresses. Finally, the application of the CNTs/CF cross-scale interfacial reinforcement system in composites is explored, resulting in CFRP exhibiting superior properties in a number of ways, but only in terms of the mechanical properties of the system, which are exploited. Therefore, the development and application of multi-functional materials such as heat and electricity, as well as the preparation of high efficiency, high quality and low cost CNTs/CF/matrix three-phase composites will be the main development direction in the field of nano-reinforced CF composites in the future.

Joint Traffic Routing and Scheduling Algorithm Eliminating the Nondeterministic Interruption for TSN Networks Used in IIoT

Recently, time-sensitive networking (TSN) has been developed to incorporate real-time capabilities to the standard Ethernet for the incompatible issue in existing industrial communication solutions and is considered a promising solution to satisfy the requirements of the Industrial Internet of Things (IIoT). However, the routing and scheduling methods are left out of the TSN standard. This study developed a novel joint traffic routing and scheduling algorithm to compute routes for time-critical flows and construct the schedules. The validity of the proposed algorithm was verified using a random synthetic test scenario. The simulation results demonstrated that the real-time requirements of time-critical traffic could be satisfied with the proposed algorithm. Subsequently, we evaluated the scalability of the proposed algorithm from the perspective of the number of flows that can be scheduled and the network size. The results showed that the computational times for 4000 flows in the realistic industrial network topology and random network topologies (with up to 21 switches and 105 end stations) are at the subsecond level, which indicated the perfect scalability of the proposed algorithm. In addition, compared with the well-known integer linear programming (ILP)-based approach, Degree of Conflict (DoC)-aware iterative routing and scheduling (DA/IRS) approach, and hybrid genetic algorithm (HGA)-based approach, the proposed algorithm is faster than the three approaches by at least an order of magnitude.

A Lightweight Energy-Efficient Algorithm for mitigation and isolation of Internal Rank Attackers in RPL based Internet of Things

Internet of Things (IoT) has become a pivotal technology for connecting various physical things or devices to the internet. Despite the potential advantages, security in IoT network continues to be a major concern due to enormous volume of the data generated and transmitted. Since the devices in IoT are resource-constrained and heterogeneous, it is becoming harder to maintain security in routing protocols such as Routing Protocol for Low-power and Lossy networks (RPL). RPL uses control packets for the construction of tree topology and it is prone to various attacks such as flood, version, rank and so on. Rank attack leads to the formation of loop, increased latency in packet delivery, energy depletion in nodes, decreased delivery ratio of packets and so on. Since the impact of the rank attack is high on the network, a lightweight energy-efficient algorithm is needed for the detection and isolation of rank attackers. The proposed Rank Attack Detection (RAD) algorithm uses a non-cryptographic hash algorithm to preserve the integrity of control packets. It uses random sampling to reduce energy consumption. Once the rank attacker is detected, an alarm is generated against the attackers. The alarm is not a separate control packet; it is appended to the control message itself. The proposed algorithm is analysed using different topologies by comparing it with the already existing algorithms in terms of control packet overhead, delivery ratio, latency for delivery of data packets, accuracy and energy consumption. It achieves better accuracy in grid-centered topology and the average energy consumption is reduced in random topology.

Optimal operation for marine hybrid power system: Novel optimization algorithm and comprehensive analysis for penalty functions

In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv) is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS.

Robust event-triggered cooperative control of uncertain multiple Euler–Lagrange systems based on three kinds of random transformation topologies

In this paper, we consider the consensus of uncertain multiple Euler–Lagrange​ systems. Based on switching topologies, the time-varying delay compensation, event-triggered mechanism, and finite-time convergence are considered, which can satisfy the needs of an actual system. First, the nonlinear uncertainties of the system are well approximated by applying the neural-networked scheme. Second, the new event-triggered mechanism can avoid the influence of the limited signal bandwidth and effectively decrease the data transmission times. Third, an adaptive law is used to compensate for the model and external disturbances. Fourth, the proposed switching topology scheme can respond to different scenarios, including when some agents fail to suddenly connect with their neighbors, and meet a wider range of collaborative needs. The effectiveness and feasibility of the proposed technique are demonstrated in theorem analysis and simulations.