Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers.In order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface codes.This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions.Compared to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities.By reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption costs.In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization.Compared to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation.

Stressed-Deformed State of Ice Crossings at the Surface Reinforcement of Composite Materials

Ice crossings have been used for several reasons.First,due to the active development of the Arctic shelf,supplies and minerals are provided and transferred on special transports on the surface of ice covers.Second,ice crossings across rivers are used to reduce the length of transport routes.Traditional methods of increasing the load bearing capacity of ice are ice freezing from above,ice freezing from below,and ice strengthening through a wooden copepod flooring.Practical experience shows that the physical and mechanical properties of ice covers are unreliable and changeable in time and strongly depend on various external factors.Therefore,ice covers should be strengthened through alternative methods.Thus,predicting the bearing capacity of ice crossings and exploring methods for their strengthening are important.In this study,we consider the results of experimental and numerical studies on the bearing and deformation capacity of ice beams upon destruction from pure bending.Under pure bending,ice breaks down in the ice crossing when transports move along it.Tests were carried out with a specified reinforcement scheme.The results of the model experiments were compared with numerical calculations in the ANSYS software package.Experiments on ice beams reinforced with various composite materials were also performed.Destruction of samples in all cases occurred as a result of the formation of extensive cracks in the ice caused by the bending moment in the middle of the beam span.Based on the experimental and numerical research results,the use of a surface reinforcement in ice with various materials can increase the bearing capacity from 65%to 99%for this reinforcement scheme.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.

Surface effects on a mode-III reinforced nano-elliptical hole embedded in one-dimensional hexagonal piezoelectric quasicrystals

To effectively reduce the field concentration around a hole or crack,an anti-plane shear problem of a nano-elliptical hole or a nano-crack pasting a reinforcement layer in a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)is investigated subject to remotely mechanical and electrical loadings.The surface effect and dielectric characteristics inside the hole are considered for actuality.By utilizing the technique of conformal mapping and the complex variable method,the phonon stresses,phason stresses,and electric displacements in the matrix and reinforcement layer are exactly derived under both electrically permeable and impermeable boundary conditions.Three size-dependent field intensity factors near the nano-crack tip are further obtained when the nano-elliptical hole is reduced to the nano-crack.Numerical examples are illustrated to show the effects of material properties of the surface layer and reinforced layer,the aspect ratio of the hole,and the thickness of the reinforcing layer on the field concentration of the nano-elliptical hole and the field intensity factors near the nano-crack tip.The results indicate that the properties of the surface layer and reinforcement layer and the electrical boundary conditions have great effects on the field concentration of the nano-hole and nano-crack,which are useful for optimizing and designing the microdevices by PQC nanocomposites in engineering practice.

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

Microstructure and wear characteristics of ATZ ceramic particle reinforced gray iron matrix surface composites

The alumina toughened zirconia(ATZ) ceramic particle reinforced gray iron matrix surface composite was successfully manufactured by pressureless infi ltration. The porous preform played a key role in the infi ltrating progress. The microstructure was observed by scanning electron microscopy(SEM); the phase constitutions was analyzed by X-ray diffraction(XRD); and the hardness and wear resistance of selected specimens were tested by hardness testing machine and abrasion testing machine, respectively. The addition of high carbon ferrochromium powders leads to the formation of white iron during solidifi cation. The wear volume loss rates of ATZ ceramic particle reinforced gray iron matrix surface composite decreases fi rst, and then tends to be stable. The wear resistance of the composite is 2.7 times higher than that of gray iron matrix. The reason is a combination of the surface hardness increase of gray iron matrix and ATZ ceramic particles and alloy carbides protecting effect on gray iron matrix.

Quasi-plane-hypothesis of strain coordination for RC beams seismically strengthened with externally-bonded or near-surface mounted fiber reinforced plastic

The application of fiber reinforced plastic（FRP）,including carbon FRP and glass FRP,for structural repair and strengthening has grown due to their numerous advantages over conventional materials such as externally bonded reinforcement（EBR） and near-surface mounted（NSM） strengthening techniques.This paper summarizes the results from 21 reinforced concrete beams strengthened with different methods,including externally-bonded and near-surface mounted FRP,to study the strain coordination of the FRP and steel rebar of the RC beam.Since there is relative slipping between the RC beam and the FRP,the strain of the FRP and steel rebar of the RC beam satisfy the quasi-plane-hypothesis;that is,the strain of the longitudinal fiber that parallels the neutral axis of the plated beam within the scope of the effective height（h 0） of the cross section is in direct proportion to the distance from the fiber to the neutral axis.The strain of the FRP and steel rebar satisfies the equation：ε FRP =βε steel,and the value of β is equal to 1.1-1.3 according to the test results.

A COMPACT AND HANDHELD SURFACE PENETRATING RADAR FOR THE DETECTION OF REINFORCED CONCRETE STRUCTURES

Surface Penetrating Radar (SPR) is a recently developed technology for non-destructive testing. It can be used to image and interpret the inner structure of the reinforced concrete. This paper gives the details about a compact and handheld SPR developed recently for reinforced concrete structure detection. The center operation frequency of the radar is 1.6 GHz. Not only it has fast acquisition ability, but also it can display the testing result on the LCD screen in real-time. The testing results show that the radar has a penetrating range of more than 30 cm, and a lateral resolution better than 5 cm. The performance validates that the radar can meet the application requirements for reinforced concrete structure detection.

Optimization of process parameters for in situ casting of Al/TiB_2 composites through response surface methodology

The mathematical models were developed to predict the ultimate tensile strength （UTS） and hardness of Al/TiB2 MMCs fabricated by in situ reaction process. The process parameters include temperature, reaction time and mass fraction of TiB2. The in-situ casting was carried out based on three-factor five-level central composite rotatable design using response surface methodology （RSM）. The validation of the model was carried out using ANOVA. The mathematical models developed for the mechanical properties were predicted at 95% confidence limit.

Shaking table test and numerical simulation on the effect of reinforcement on the seismic safety of dam slopes

The seismic safety of the reinforcement dam slope is studied through shaking table test and numerical simulation.The dynamic characteristics of dam slopes,failure mechanism,seismic stability,as well as the effect of reinforcement during earthquakes are discussed.An elasto-plastic analysis method (FLAC) is used to simulate the dynamic failure process of the reinforcement dam slope.The change law of permanent displacement of dam slope is studied.The effect of the length and the space of reinforcement on the depth of slip surface and the slope stability are investigated.Good agreement is obtained between the numerical results and those from shaking table tests.The results show that the dynamic failure is a gradual process not at a particular time.With the increase of the reinforcement length or the decreasing reinforcement spacing,the slip surface becomes deeper and thus the slope stability is improved.The reinforcement can obviously enhance the overall stability of slope dam.It can also effectively control the shallow sliding of slope.These researches provide basic data for reinforcement measures design of earth-rockfill dam.

AIRIS:Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions.

Deep Reinforcement Learning Based Power Minimization for RIS-Assisted MISO-OFDM Systems

In this paper,we investigate the downlink orthogonal frequency division multiplexing(OFDM)transmission system assisted by reconfigurable intelligent surfaces(RISs).Considering multiple antennas at the base station(BS)and multiple single-antenna users,the joint optimization of precoder at the BS and the phase shift design at the RIS is studied to minimize the transmit power under the constraint of the certain quality-of-service.A deep reinforcement learning(DRL)based algorithm is proposed,in which maximum ratio transmission(MRT)precoding is utilized at the BS and the twin delayed deep deterministic policy gradient(TD3)method is utilized for RIS phase shift optimization.Numerical results demonstrate that the proposed DRL based algorithm can achieve a transmit power almost the same with the lower bound achieved by manifold optimization(MO)algorithm while has much less computation delay.

Deep Reinforcement Learning for IRS-Assisted UAV Covert Communications

Covert communications can hide the existence of a transmission from the transmitter to receiver.This paper considers an intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)covert communication system.It was inspired by the high-dimensional data processing and decisionmaking capabilities of the deep reinforcement learning(DRL)algorithm.In order to improve the covert communication performance,an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network(TAP-DDQN)is proposed.The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system,compared with benchmark solutions.

Discrete Phase Shifts Control and Beam Selection in RIS-Aided MISO System via Deep Reinforcement Learning

Reconfigurable intelligent surface(RIS)for wireless networks have drawn lots of attention in both academic and industry communities.RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction,thus it provides supplementary links for wireless networks.Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts,but phase shifts of RIS are discrete in practical hardware.Thus we focus on the actual discrete phase shifts on RIS in this paper.Using the advanced deep reinforcement learning(DRL),we jointly optimize the transmit beamforming matrix from the discrete Fourier transform(DFT)codebook at the base station(BS)and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio(SINR).Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts,the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network.Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.

Microstructure and high-temperature wear properties of in situ TiC composite coatings by plasma transferred arc surface alloying on gray cast iron

In this work, an in situ synthesized TiC-reinforced metal matrix composite （MMC） coating of approximately 350-400μm thickness was fabricated on a gray cast iron （GCI） substrate by plasma transferred arc （PTA） surface alloying of Ti-Fe alloy powder. Microhard- ness tests showed that the surface hardness increased approximately four-fold after the alloying treatment. The microstructure of the MMC coating was mainly composed of residual austenite, acicular martensite, and eutectic ledeburite. Scanning electron microscopy （SEM） and X-ray diffraction analyzes revealed that the in situ TiC particles, which were formed by direct reaction of Ti with carbon originally contained in the GCI, was uniformly distributed at the boundary of residual anstenite in the alloying zone. Pin-on-disc high-temperature wear tests were performed on samples both with and without the MMC coating at room temperature and at elevated temperatures （473 K and 623 K）, and the wear behavior and mechanism were investigated. The results showed that, after the PTA alloying treatment, the wear resistance of the sam- ples improved significantly. On the basis of our analysis of the composite coatings by optical microscopy, SEM with energy-dispersive X-ray spectroscopy, and microhardness measurements, we attributed this improvement of wear resistance to the transformation of the microstruc- ture and to the presence of TiC particles.

A review on COLREGs-compliant navigation of autonomous surface vehicles:From traditional to learning-based approaches

A growing interest in developing autonomous surface vehicles(ASVs)has been witnessed during the past two decades,including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways.This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches.It features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path replanning.The existing methods in all these three stages are classified according to various criteria.An in-time overview of the recently-developed learning-based methods in motion prediction and path replanning is provided,with a discussion on ASV navigation scenarios and tasks where learning-based methods may be needed.Finally,more general challenges and future directions of ASV navigation are highlighted.

RIS-Assisted UAV-D2D Communications Exploiting Deep Reinforcement Learning

Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been regarded as promising techniques for next-generation communications.To mitigate the strong interference caused by the line-of-sight(LoS)airto-ground channels,we deploy a reconfigurable intelligent surface(RIS)to rebuild the wireless channels.A joint optimization problem of the transmit power of UAV,the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service(QoS)requirement of cellular users.Due to the high channel dynamics and the coupling among cellular users,the RIS,and the D2D users,it is challenging to find a proper solution.Thus,a RIS softmax deep double deterministic(RIS-SD3)policy gradient method is proposed,which can smooth the optimization space as well as reduce the number of local optimizations.Specifically,the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced.Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user.Moreover,the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic(TD3)policy gradient algorithm in a dynamic environment.

A review on machinability of carbon fiber reinforced polymer(CFRP)and glass fiber reinforced polymer(GFRP)composite materials

Fiber reinforced polymer(FRP) composite materials are heterogeneous and anisotropic materials that do not exhibit plastic deformation. They have been used in a wide range of contemporary applications particularly in space and aviation,automotive,maritime and manufacturing of sports equipment. Carbon fiber reinforced polymer(CFRP) and glass fiber reinforced polymer(GFRP) composite materials,among other fiber reinforced materials,have been increasingly replacing conventional materials with their excellent strength and low specific weight properties. Their manufacturability in varying combinations with customized strength properties,also their high fatigue,toughness and high temperature wear and oxidation resistance capabilities render these materials an excellent choice in engineering applications.In the present review study,a literature survey was conducted on the machinability properties and related approaches for CFRP and GFRP composite materials. As in the machining of all anisotropic and heterogeneous materials,failure mechanisms were also reported in the machining of CFRP and GFRP materials with both conventional and modern manufacturing methods and the results of these studies were obtained by use of variance analysis(ANOVA),artificial neural networks(ANN) model,fuzzy inference system(FIS),harmony search(HS) algorithm,genetic algorithm(GA),Taguchi's optimization technique,multi-criteria optimization,analytical modeling,stress analysis,finite elements method(FEM),data analysis,and linear regression technique. Failure mechanisms and surface quality is discussed with the help of optical and scanning electron microscopy,and profilometry. ANOVA,GA,FEM,etc. are used to analyze and generate predictive models.

Seismic responses of the steel-strip reinforced soil retaining wall with full-height rigid facing from shaking table test

To investigate the seismic response of the steel-strip reinforced soil retaining wall with fullheight rigid facing in terms of the acceleration in the backfill, dynamic earth pressure in the backfill, the displacements on the facing and the dynamic reinforcement strain distribution under different peak acceleration, a large 1-g shaking table test was performed on a reduced-scale reinforced-earth retaining wall model. It was observed that the acceleration response in non-strip region is greater than that in potential fracture region which is similar with the stability region under small earthquake,while the acceleration response in potential fracture region is greater than that in stability region in middle-upper of the wall under moderately strong earthquakes. The potential failure model of the rigid wall is rotating around the wall toe. It also was discovered that the Fourier spectra produced by the inputting white noises after seismic wave presents double peaks, rather than original single peak, and the frequency of the second peak trends to increase with increasing the PGA(peak ground amplitude) of the excitation which is greater than 0.4 g. Additionally,the non-liner distribution of strip strain along the strips was observed, and the distribution trend was not constant in different row. Soil pressure peak value in stability region is larger than that in potential fracture region. The wall was effective under 0.1 g-0.3 g seismic wave according to the analyses of the facing displacement and relative density. Also, it was discovered that the potential failure surface is corresponds to that in design code, but the area is larger. The results from the study can provide guidance for a more rational design of reinforced earth retaining walls with full-height rigid facing in the earthquake zone.