Resilient Satellite Communication Networks Towards Highly Dynamic and Highly Reliable Transmission

As the key infrastructure of space-ground integrated information networks,satellite communication networks provide high-speed and reliable information transmission.In order to meet the burgeoning service demands of the IoT and the Internet,the low-latency LEO satellite network has developed rapidly.However,LEO satellites face inherent problems such as small coverage,fast moving speed and short overhead time,which will be more severe when serving high-dynamic users,e.g.high-speed rails and airplanes.The heterogeneous network composed of GEO,MEO and LEO satellites can provide various services,whose network management and resource allocation are also more challenging.

Constructing Anisotropic Conductive Networks inside Hollow Elastic Fiber with High Sensitivity and Wide‑Range Linearity by Cryo‑spun Drying Strategy

Stretchable conductive fibers composed of conductive materials and elastic substrates have advantages such as braiding abil-ity,electrical conductivity,and high resilience,making them ideal materials for fibrous wearable strain sensors.However,the weak interface between the conductive materials and elastic substrates restricts fibers flexibility under strain,leading to challenges in achieving both linearity and sensitivity of the as-prepared fibrous strain sensor.Herein,cryo-spun drying strategy is proposed to fabricate the thermoplastic polyurethane(TPU)fiber with anisotropic conductive networks(ACN@TPU fiber).Benefiting from the excellent mechanical properties of TPU,and the excellent interface among TPU,silver nanoparticles(AgNPs)and polyvinyl alcohol(PVA),the prepared ACN@TPU fiber exhibits an outstanding mechanical performance.The anisotropic conductive networks enable the ACN@TPU fiber to achieve high sensitivity(gauge factor,GF=4.68)and excellent linearity within a wide working range(100%strain).Furthermore,mathematical model based on AgNPs is established and the resistance calculation equation is derived,with a highly matched fitting and experimental results(R2=0.998).As a conceptual demonstration,the ACN@TPU fiber sensor is worn on a mannequin to accurately and instantly detect movements.Therefore,the successful construction of ACN@TPU fiber with anisotropic conductive networks through the cryo-spun drying strategy provides a feasible approach for the design and preparation of fibrous strain sensing materials with high linearity and high sensitivity.

Fitting V F Converter*ss Output Using High Order Neural Networks

A new method is presented in this paper for fitting VFC*ss (voltage to frequency converter) output functions by using high order neural networks. The nonlinear estimation is implemented when the VFC110 is used at a full scale output frequency of 4 MHz. Two kinds of on line dynamic calibrating circuits are designed to improve the sampling precision. This method can also be applied to different industrial applications.

A Double Network Hydrogel with High Mechanical Strength and Shape Memory Properties

Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.

High Capacity Mobile Ad Hoc Network Using THz Frequency Enhancement

We propose a new design of the high channel capacity in Mobile Ad Hoc Network that uses the dense wave-length division multiplexing wavelength enhancement, in which the increasing in channel capacity and sig-nal security can be provided. The increasing in number of channel can be obtained by the increasing in wavelength density, while the security is introduced by the specific wavelength filter, which is operated by the Ad Hoc node operator and link with other nodes in coverage by dedicated one-to-one in direct or relay node. The optical communication wavelength enhancement is reviewed. The advantage is that the proposed system can be implemented and used incorporating with the existed communication link in both infrastruc-ture-based and Ad Hoc networks wireless network, where the privacy can be provided, which is discussed in details.

High Frequency Communication Network with Diversity: System Structure and Key Enabling Techniques

High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication network(HFCN) in this paper.First, we present the benefits and the challenges by introducing diversity technology into the existing HFCN. Secondly, to exploit the benefits fully and overcome the challenges, we propose a system structure suitable for deploying diversity technology in HFCN in large scale,based on the cloud radio access network and software defined network. Moreover, we present a general structure for the real-time updating frequency management system that plays a more important role especially when resource consuming(e.g., frequency) diversity technology is deployed. Thirdly, we investigate the key techniques enabling diversity technology deployment. Finally, we point out the future research directions to help the HFCN with diversity work more efficiently and intelligently.

Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping （neuro-SM） technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.

Optimal design study of high order FIR digital filters based on neural network algorithm

An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved, and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass, bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely .The presented optimal design approach of high order FIR digital filter is significantly effective.

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

Fuzzy neural network analysis on gray cast iron with high tensile strength and thermal conductivity

To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.

Prediction of flow stresses at high temperatures with artificial neural networks

On the basis of the data obtained on Gleeble 1500 Thermal Simulator, the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al Li alloy have been developed with BP artificial neural networks method. The results show that the model on basis of BPNN is practical and it reflects the actual feature of the deforming process. It states that the difference between the actual value and the output of the model is in order of 5%. [

Flux-measuring approach of high temperature metal liquid based on BP neural networks

A soft-measuring approach is presented to measure the flux of liquid zinc with high temperature andcausticity. By constructing mathematical model based on neural networks, weighing the mass of liquid zinc, the fluxof liquid zinc is acquired indirectly, the measuring on line and flux control are realized. Simulation results and indus-trial practice demonstrate that the relative error between the estimated flux value and practical measured flux value islower than 1.5%, meeting the need of industrial process.

Altered amplitude of low-frequency fluctuations and default mode network connectivity in high myopia:a resting-state fMRI study

AIM:To analyze changes in amplitude of low-frequency fluctuations(ALFFs)and default mode network(DMN)connectivity in the brain,using resting-state functional magnetic resonance imaging(rs-fMRI),in high myopia(HM)patients.METHODS:Eleven patients with HM(HM group)and 15 age-and sex-matched non-HM controls(non-HM group)were recruited.ALFFs were calculated and compared between HM group and non-HM group.Independent component analysis(ICA)was conducted to identify DMN,and comparisons between DMNs of two groups were performed.Region-of-interest(ROI)-based analysis was performed to explore functional connectivity(FC)between DMN regions.RESULTS:Significantly increased ALFFs in left inferior temporal gyrus(ITG),bilateral rectus gyrus(REC),bilateral middle temporal gyrus(MTG),left superior temporal gyrus(STG),and left angular gyrus(ANG)were detected in HM group compared with non-HM group(all P<0.01).HM group showed increased FC in the posterior cingulate gyrus(PCC)/precuneus(preCUN)and decreased FC in the left medial prefrontal cortex(mPFG)within DMN compared with nonHM group(all P<0.01).Compared with non-HM group,HM group showed higher FC between mPFG and bilateral middle frontal gyrus(MFG),ANG,and MTG(all P<0.01).In addition,HM patients showed higher FC between PCC/(preCUN)and the right cerebellum,superior frontal gyrus(SFG),left pre CUN,superior frontal gyrus(SFG),and medial orbital of the superior frontal gyrus(ORB supmed;all P<0.01).CONCLUSION:HM patients show different ALFFs and DMNs compared with non-HM subjects,which may imply the cognitive alterations related to HM.

Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.

High－Performance Network Communications for CSCW

Computer supported cooperative work as a new multi disciplinary research area, brings a lot of new challenges for the computer technologies. Having analyzed the CSCW applications by using the groupware space time matrix, we conclude that the communicatio

High-resolution Image Reconstruction by Neural Network and Its Application in Infrared Imaging

As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Modeling of the Coupling of the Lightning Shock Wave Flow Circuit in the High Voltage Electrical Network

This article focuses on the aggression of lightning overload on the equipment of the electrical network of sites where storm activity is very dense;and the electrocution of people located in the direct environment of the high-voltage substation during the flow of lightning current to the ground through the ground socket. The modeling of the flow circuit of the shock wave consisting of guard wire, lightning arrester and ground socket couple to the transformer of the high voltage substations, thanks to the approach of a servo block, led to the synthesis of a PID regulator (corrector) whose action is to reject the effects of the overvoltage on the network equipment and to significantly reduce or even cancel the effects of the step or touch voltage due to the distribution of the potential around the ground socket;and thus improve the quality of service of the high-voltage transmission and distribution electricity network, especially in stormy times.

Design of High Precision Horizontal Control Network for Large-Scale Hydropower Project

A new solution of combination network of GPS and high precise distance measurements with EDM is proposed. Meanwhile, it’s inadvisable only using GPS network without distance measurements. Three schemes: terrestrial network, GPS network and combination network are discussed for horizontal control network design of Xiangjiaba Dam in view of precision, reliability, coordinate and outlay in detail.