Research on Network Cognition Model and Mechanism of Intelligent Information Network

Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.

The mechanisms,evaluation and estimation of anti-skid performance of snowy and icy pavement:A review

The anti-skid performance of snowy and icy pavements is a popular research topic among road workers.Snow and ice are pollutants on a road surface.They significantly reduce the skid resistance of pavements,and thus,cause traffic accidents.Pertinent research progress on the skid resistance of snowy and icy pavements was reviewed and summarized in this work.The formation and classification of snowy and icy pavements were described on the basis of the state of snow and ice.The friction mechanisms between tires and snowy and icy pavements were revealed.Measurement methods and their applicability to the skid resistance of snowy and icy pavements were summarized.Factors that affect the skid resistance of pavements were discussed from the perspectives of pavement,environment,and vehicle.In addition,models of snowy and icy pavement resistance were classified into experience,mechanical,and numerical models.The advantages and disadvantages of these models were then compared and analyzed.Some suggestions regarding snowy and icy pavements were presented in accordance with the aforementioned information,including the development of efficient testing tools,the quantification of skid resistance under the coupling effects of multiple factors,the establishment of unified evaluation standards,and the development of more effective skid resistance models.

Localization for mixed near-field and far-field sources under impulsive noise

In order to solve the problem that the performance of traditional localization methods for mixed near-field sources(NFSs)and far-field sources(FFSs)degrades under impulsive noise,a robust and novel localization method is proposed.After eliminating the impacts of impulsive noise by the weighted out-lier filter,the direction of arrivals(DOAs)of FFSs can be estimated by multiple signal classification(MUSIC)spectral peaks search.Based on the DOAs information of FFSs,the separation of mixed sources can be performed.Finally,the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors.The Cramer-Rao bounds(CRB)for the unbiased estimations of DOA and range under impulsive noise have been drawn.Simulation experiments verify that the proposed method has advantages in probability of successful estimation(PSE)and root mean square error(RMSE)compared with existing localization methods.It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio(GSNR),few snapshots,and strong impulse.

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer.First,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources considerably.The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated.Second,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed.Moreover,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given explicitly.The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

Quasi-anti-parity–time-symmetric single-resonator micro-optical gyroscope with Kerr nonlinearity

Parity–time(PT) and quasi-anti-parity–time(quasi-APT) symmetric optical gyroscopes have been proposed recently which enhance Sagnac frequency splitting. However, the operation of gyroscopes at the exceptional point(EP) is challenging due to strict fabrication requirements and experimental uncertainties. We propose a new quasi-APT-symmetric micro-optical gyroscope which can be operated at the EP by easily shifting the Kerr nonlinearity. A single resonator is used as the core sensitive component of the quasi-APT-symmetric optical gyroscope to reduce the size, overcome the strict structural requirements and detect small rotation rates. Moreover, the proposed scheme also has an easy readout method for the frequency splitting. As a result, the device achieves a frequency splitting 10~5 times higher than that of a classical resonant optical gyroscope with the Earth's rotation. This proposal paves the way for a new and valuable method for the engineering of micro-optical gyroscopes.

Sensitivity enhancement of micro-optical gyro with photonic crystal

We propose a core rotation-sensing element for improving the sensitivity of the micro-optical gyroscope using the large nonreciprocal effect with a photonic crystal.The sharp transmission peak of electromagnetically induced transparency in photonic crystal generated from a periodic distribution of cold atoms is sensitive to the rotation.Our numerical results show that the sensitivity of relative rotation is about 50 times higher and the sensitivity of absolute rotation is more than two orders higher than that of the traditional resonant optical gyroscope.Also,the sensitivity of the gyroscope can be manipulated by varying the atomic density,modulation frequency,probe pulse width,and photonic crystal length,etc.

SI of Interaction energy prediction of organic molecules using Deep Tensor Neural Network

The support informations of DTNN architecture and prediction results of six testing cases in K-fold are listed here.

Thermal spin molecular logic gates modulated by an electric field

Logic gates are fundamental structural components in all modern digital electronic devices. Here, nonequilibrium Green's functions are incorporated with the density functional theory to verify the thermal spin transport features of the single-molecule spintronic devices constructed by a single molecule in series or parallel connected with graphene nanoribbons electrodes. Our calculations demonstrate that the electric field can manipulate the spin-polarized current. Then, a complete set of thermal spin molecular logic gates are proposed, including AND, OR, and NOT gates. The mentioned logic gates enable different designs of complex thermal spin molecular logic functions and facilitate the electric field control of thermal spin molecular devices.

Finite dimensional irreducible representations of Lie superalgebra D(2,1;α)

This paper focuses on the finite dimensional irreducible representations of Lie superalgebra D(2,1;α).We explicitly construct the finite dimensional representations of the superalgebra D(2,1;α)by using the shift operator and differential operator representations.Unlike ordinary Lie algebra representation,there are typical and atypical representations for most superalgebras.Therefore,its typical and atypical representation conditions are also given.Our results are expected to be useful for the construction of primary fields of the corresponding current superalgebra of D(2,1;α).

The statistical observation localized equivalent-weights particle filter in a simple nonlinear model

This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF(LPF)to construct the localized EWPF.The new approach is called the statistical observation localized EWPF(LEWPF-Sobs);it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the weights.This approach not only retains the advantages of the EWPF,but also improves the assimilation quality when using sparse observations.Numerical experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution observations.Comparisons of the statistical observation localized EWPF and LPF reveal the advantages of the new method,with fewer particles giving better results.In particular,the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities.

Torque estimation for robotic joint with harmonic drive transmission based on system dynamic characteristics

In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output.Through analyzing the structures of the harmonic drive and experiment apparatus,a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter(UKF)is designed and built.Based on research and scheme,torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique.Finally,a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed,and simulation results compared with the measurements of a commercial torque sensor,have verified the effectiveness of the proposed method.

High-Precision Dead-Time Intellectual Property Core and Its Compensation for Inverters

In the inverter circuit,there exists a specific on-off time in each power transistor.As such,to prevent a short circuit of the two switch devices on the upper and lower bridge arms,a specific dead time must be set in the pulse width modulation(PWM)and the sinusoidal pulse width modulation(SPWM)signals.In this paper,an intellectual property(IP)core that can introduce a high-precision dead time of arbitrary length into PWM or SPWM signals of the inverter is designed to increase the precision,convenience and generalization of dead time control,resulting in a boosted control accuracy of up to 10 ns.Moreover,the added Avalon bus enables IP cores to be accessed by the field programmable gate array(FPGA)processor in a standard manner and multiple IP cores of the same class can be easily incorporated.In addition,an application for setting and compensating for dead time in a three-phase inverter based on system on programmable chip(SOPC)technology is presented.With the Nios II CPU as its core,the system adopts the mean voltage compensation method to calculate the compensation voltage,and performs dead-time compensation in a feed-forward manner.The three dead-time IP cores are controlled by Avalon bus.These allow the dead time of three groups of power transistors to be accurately controlled and flexibly adjusted.The system also features the master computer communication function while boasting the advantages of flexible control,high precision and low cost.

Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

Multi-area combined economic/emission dispatch(MACEED)problems are generally studied using analytical functions.However,as the scale of power systems increases,ex isting solutions become time-consuming and may not meet oper ational constraints.To overcome excessive computational ex pense in high-dimensional MACEED problems,a novel data-driven surrogate-assisted method is proposed.First,a cosine-similarity-based deep belief network combined with a back-propagation(DBN+BP)neural network is utilized to replace cost and emission functions.Second,transfer learning is applied with a pretraining and fine-tuning method to improve DBN+BP regression surrogate models,thus realizing fast con struction of surrogate models between different regional power systems.Third,a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization poli cy is proposed to execute MACEED optimization to obtain scheduling decisions.The proposed method not only ensures the convergence,uniformity,and extensibility of the Pareto front,but also greatly reduces the computational time.Finally,a 4-ar ea 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.

Hypergraph characterizations of copositive tensors

A real symmetric tensor A=(ai_(1…im))∈R^([m,n]) is copositive(resp.,strictly copositive)if Ax^(m)≥0(resp.,Ax^(m)>0)for any nonzero nonnegative vector x∈ℝ^(n).By using the associated hypergraph of A,we give necessary and sufficient conditions for the copositivity of A.For a real symmetric tensor A satisfying the associated negative hypergraph H−(A)and associated positive hypergraph H+(A)are edge disjoint subhypergraphs of a supertree or cored hypergraph,we derive criteria for the copositivity of A.We also use copositive tensors to study the positivity of tensor systems.

Matching Algorithms of Minimum Input Selection for Structural Controllability Based on Semi-Tensor Product of Matrices

In 2011,Liu,et al.investigated the structural controllability of directed networks.They proved that the minimum number of input signals,driver nodes,can be determined by seeking a maximum matching in the directed network.Thus,the algorithm for seeking a maximum matching is the key to solving the structural controllability problem of directed networks.In this study,the authors provide algebraic expressions for matchings and maximum matchings proposed by Liu,et al.(2011)via a new matrix product called semi-tensor product,based on which the corresponding algorithms are established to seek matchings and maximum matchings in digraphs,which make determining the number of driver nodes tractable in computer.In addition,according to the proposed algorithm,the authors also construct an algorithm to distinguish critical arcs,redundant arcs and ordinary arcs of the directed network,which plays an important role in studying the robust control problem.An example of a small network from Liu’s paper is used for algorithm verification.

A unified 3D space-based navigation model for seamless navigation in indoor and outdoor

Seamless navigation has attracted lots of attention and many methods have been reported in the literature or made available as commercial applications.The process of navigation can be interpreted as a continuous movement of 3D objects from one unoccupied 3D indoor/outdoor space to another.From a technical perspective,a 3D navigation model is one of the critical components that should be available to perform successful navigation.A major approach to build a unified navigation model to support seamless path computation is linking indoor navigation networks to outdoor road/street-based networks.Because of different sources of indoor and outdoor navigation networks,the major approach fails to build up true seamless navigation models.With regards to this,we propose a unified 3D space-based navigation model(U3DSNM).The presented model ensures all types of spaces for navigation(indoor,semi-indoor,semi-outdoor,and outdoor)have the same representation,management methods,and network derivation approach,thereby building up unified navigation networks to support seamless navigation paths planning.The model can be linked to the international standards(data models)that are also based on spaces,such as IndoorGML and the on-going version of CityGML 3.0.Three navigation path planning cases show the feasibility of U3DSNM.

A combination weighting model based on iMOEA/D-DE

This paper proposes a combination weighting(CW)model based on iMOEA/D-DE(i.e.,improved multiobjective evolutionary algorithm based on decomposition with differential evolution)with the aim to accurately compute the weight of evaluation methods.Multi-expert weight considers only subjective weights,leading to poor objectivity.To overcome this shortcoming,a multiobjective optimization model of CW based on improved game theory is proposed while considering the uncertainty of combination coefficients.An improved mutation operator is introduced to improve the convergence speed,and thus better optimization results are obtained.Meanwhile,an adaptive mutation constant and crossover probability constant with self-learning ability are proposed to improve the robustness of MOEA/D-DE.Since the existing weight evaluation approaches cannot evaluate weights separately,a new weight evaluation approach based on relative entropy is presented.Taking the evaluation method of integrated navigation systems as an example,certain experiments are carried out.It is proved that the proposed algorithm is effective and has excellent performance.

Multi-UAV Synchronous Approaching Using Homotopy-Based Trajectory Planning

Synchronous approaching is an important capability for autonomous cooperation of multiple unmanned aerial vehicles(UAVs).In this paper,a homotopy-based trajectory planning method is presented for the multi-UAV synchronous approaching problem.A homotopic trajectory description is employed to construct the trajectory solution space of the UAVs.A novel onionlike homotopy structure is proposed to decouple the performance indexes of the trajectory planning problem.Local trajectory homotopy structures are designed based on the detouring model and the hovering model of UAV.The optimal trajectories for synchronous approaching are searched within the homotopy structures.Simulation results show how synchronous the UAVs are,by using the proposed homotopy-based trajectory planning method.

A partition approach for robust gait recognition based on gait template fusion

Gait recognition has significant potential for remote human identification,hut it is easily influenced by identity-unrelated factors such as clothing,carrying conditions,and view angles.Many gait templates have been presented that can effectively represent gait features.Each gait template has its advantages and can represent different prominent information.In this paper,gait template fusion is proposed to improve the classical representative gait template(such as a gait energy image)which represents incomplete information that is sensitive to changes in contour.We also present a partition method to reflect the different gait habits of different body parts of each pedestrian.The fused template is cropped into three parts(head,trunk,and leg regions)depending on the human body,and the three parts are then sent into the convolutional neural network to learn merged features.We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones.The results show good accuracy and robustness of the proposed method for gait recognition.