Effect of degree correlation on edge controllability of real networks

We use the controllability limit theory to study impact of correlation between in- and out-degrees (degree correlation) on edge controllability of real networks. Simulation results and analytic calculations show that the degree correlation plays an important role in the edge controllability of real networks, especially dense real networks. The upper and lower controllability limits hold for all kinds of real networks. Any edge controllability in between the limits is achievable by properly adjusting the degree correlation. In addition, we find that the edge dynamics in some real networks with positive degree correlation may be difficult to control, and explain the rationality of this anomaly based on the controllability limit theory.

Symmetry-controlled edge states in graphene-like topological sonic crystal

Unique topological states emerged in various topological insulators (TI) have been proved with great application value for robust wave regulation. In this work, we demonstrate the parity inversion related to the definition of the primitive cell in one common lattice, and realize a type of symmetry-controlled edge states confined on the zigzag interfaces of the graphene-like sonic topological crystal. By simply sliding the selected 'layer' near the interface, the coupling of the pseudospin states induced by the multiple scattering for the C6v lattice results in the adjustment of the edge states. Based on the physics of the states, we experimentally propose a prototype of acoustic topological filter hosting multiple channels with independent adjustable edge states and realize the selective high transmission. Our work diversifies the prospects for the applications of the gapped edge states in the robust wave regulation, and proposes a frame to design new topological devices.

Femtosecond-laser sharp shaping of millimeter-scale geometries with vertical sidewalls

As femtosecond(fs)laser machining advances from micro/nanoscale to macroscale,approaches capable of machining macroscale geometries that sustain micro/nanoscale precisions are in great demand.In this research,an fs laser sharp shaping approach was developed to address two key challenges in macroscale machining(i.e.defects on edges and tapered sidewalls).The evolution of edge sharpness(edge transition width)and sidewall tapers were systematically investigated through which the dilemma of simultaneously achieving sharp edges and vertical sidewalls were addressed.Through decreasing the angle of incidence(AOI)from 0◦to−5◦,the edge transition width could be reduced to below 10µm but at the cost of increased sidewall tapers.Furthermore,by analyzing lateral and vertical ablation behaviors,a parameter-compensation strategy was developed by gradually decreasing the scanning diameters along depth and using optimal laser powers to produce non-tapered sidewalls.The fs laser ablation behaviors were precisely controlled and coordinated to optimize the parameter compensations in general manufacturing applications.The AOI control together with the parameter compensation provides a versatile solution to simultaneously achieve vertical sidewalls as well as sharp edges of entrances and exits for geometries of different shapes and dimensions.Both mm-scale diameters and depths were realized with dimensional precisions below 10µm and surface roughness below 1µm.This research establishes a novel strategy to finely control the fs laser machining process,enabling the fs laser applications in macroscale machining with micro/nanoscale precisions.

A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid

Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.

A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid

Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.