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.

Modeling and Experiment of a Morphing Wing Integrated with a Trailing Edge Control Actuation System

Morphing wing has attracted many research attention and effort in aircraft technology development because of its advantage in lift to draft ratio and flight performance.Morphing wing technology combines the lift and control surfaces into a seamless wing and integrates the primary structure together with the internal control system.It makes use of the wing aeroelastic deformation induced by the control surface to gain direct force control through desirable redistribution of aerodynamic forces.However some unknown mechanical parameters of the control system and complexity of the integrated structure become a main challenge for dynamic modeling of morphing wing.To solve the problem,a method of test data based modal sensitivity analysis is presented to improve the morphing wing FE model by evaluating the unknown parameters and identifying the modeling boundary conditions.An innovative seamless morphing wing with the structure integrated with a flexible trailing edge control system is presented for the investigation.An experimental model of actuation system driven by a servo motor for the morphing wing is designed and established.By performing a vibration test and the proposed modal sensitivity analysis,the unknown torsional stiffness of the servo motor and the boundary condition of the actuation mechanism model is identified and evaluated.Comparing with the test data,the average error of the first four modal frequency of the improved FE model is reduced significantly to less than 4%.To further investigate the morphing wing modeling,a wing box and then a whole morphing wing model including the skin and integrated with the trailing edge actuation system are established and tested.By using the proposed method,the FE model is improved by relaxing the constraint between the skin and actuation mechanism.The results show that the average error of the first three modal frequency of the improved FE model is reduced to less than 6%.The research results demonstrate that the presented seamless morphing wing integrated with a flexible trailing edge control surface can improve aerodynamic characteristics.By using the test data based modal sensitivity analysis method,the unknown parameter and boundary condition of the actuation model can be determined to improve the FE model.The problem in dynamic modeling of high accuracy for a morphing wing can be solved in an effective manner.

Research of the Control Domain of Edges in Regression Testing

Regression testing is the process of validating modified software to provide confidence that the changed parts of the software behave as intended and that the unchanged parts have not been adversely affected by the modifications. The goal of regression testing is to reduce the test suit by testing the new characters and the modified parts of a program with the original test suit. Regression testing is a high cost testing method. This paper presents a regression testing selection technique that can reduce the test suit on the basis of Control Flow Graph (CFG). It import the inherit strategy of object-oriented language to ensure an edge’s control domain to reduce the test suit size effectively. We implement the idea by coding the edge. An algorithm is also presented at last.

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.

Transverse thickness difference control technology for non-oriented silicon steel

Transverse thickness difference is an important quality index of non-oriented silicon steel strips. In order to fulfill users＇ accuracy requirements on the transverse thickness of silicon steel and improve the production yield, the factors influencing transverse thickness difference were analyzed. Then the work roll shape, control strategy and incoming hot-rolled strips were optimized. Since the optimization measures were implemented in the actual production, the thickness difference of non-oriented silicon steel has been reduced greatly and fulfilled the requirements placed by users. These measures have achieved remarkable effects.

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.

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.

Numerical Study of Improving Aerodynamic Performance of Low Solidity LPT Cascade through Increasing Trailing Edge Thickness

This paper presents a new idea to reduce the solidity of low-pressure turbine(LPT) blade cascades,while remain the structural integrity of LPT blade.Aerodynamic performance of a low solidity LPT cascade was improved by increasing blade trailing edge thickness(TET).The solidity of the LPT cascade blade can be reduced by about12.5% through increasing the TET of the blade without a significant drop in energy efficiency.For the low solidity LPT cascade,increasing the TET can decrease energy loss by 23.30% and increase the flow turning angle by1.86% for Reynolds number(Re) of 25,000 and freestream turbulence intensities(FSTT) of 2.35%.The flow control mechanism governing behavior around the trailing edge of an LPT cascade is also presented.The results show that appropriate TET is important for the optimal design of high-lift load LPT blade cascades.