Obtaining vehicle parameters from bridge dynamic response:a combined semi-analytical and particle filtering approach

Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.

Research on Dynamic Mathematical Resource Screening Methods Based on Machine Learning

The current digital educational resources are of many kinds and large quantities, to solve the problems existing in the existing dynamic resource selection methods, a dynamic resource selection method based on machine learning is proposed. Firstly, according to the knowledge structure and concepts of mathematical resources, combined with the basic components of dynamic mathematical resources, the knowledge structure graph of mathematical resources is constructed;according to the characteristics of mathematical resources, the interaction between users and resources is simulated, and the graph of the main body of the resources is identified, and the candidate collection of mathematical knowledge is selected;finally, according to the degree of matching between mathematical literature and the candidate collection, machine learning is utilized, and the mathematical resources are screened.

A novel method for predicting the power outputs of wave energy converters

This paper focuses on realistically predicting the power outputs of wave energy converters operating in shallow water nonlinear waves. A heaving two-body point absorber is utilized as a specific calculation example, and the generated power of the point absorber has been predicted by using a novel method（a nonlinear simulation method） that incorporates a second order random wave model into a nonlinear dynamic filter. It is demonstrated that the second order random wave model in this article can be utilized to generate irregular waves with realistic crest–trough asymmetries, and consequently, more accurate generated power can be predicted by subsequently solving the nonlinear dynamic filter equation with the nonlinearly simulated second order waves as inputs. The research findings demonstrate that the novel nonlinear simulation method in this article can be utilized as a robust tool for ocean engineers in their design, analysis and optimization of wave energy converters.

A novel chaotic stream cipher and its application to palmprint template protection

Based on a coupled nonlinear dynamic filter （NDF）, a novel chaotic stream cipher is presented in this paper and employed to protect palmprint templates. The chaotic pseudorandom bit generator （PRBG） based on a coupled NDF, which is constructed in an inverse flow, can generate multiple bits at one iteration and satisfy the security requirement of cipher design. Then, the stream cipher is employed to generate cancelable competitive code palmprint biometrics for template protection. The proposed cancelable palmprint authentication system depends on two factors： the palmprint biometric and the password/token. Therefore, the system provides high-confidence and also protects the user＇s privacy. The experimental results of verification on the Hong Kong PolyU Palmprint Database show that the proposed approach has a large template re-issuance ability and the equal error rate can achieve 0.02%. The performance of the palmprint template protection scheme proves the good practicability and security of the proposed stream cipher.

Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.

Bidirectional Feedback Dynamic Particle Filter with Big Data for the Particle Degeneracy Problem

Particle Filter （PF） is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF （B3DPF） is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies （sensor data management, parameters used in the weight computation, resampling） of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.

Experimental realization of a switchable filter based on a dynamically transformable array

We introduce a geometrically reconfigurable metasurface whose artificial ＂atoms＂ will reorient within unit ceils in response to a thermal stimulus in the microwave spectrum. It can alternate between two contrasting behaviors under different temperatures and serve as a switchable filter that allows the incident energy to be selectively transmitted or reflected with an excess of 10 dB isolation at certain frequencies for both polarizations. The ex- perimental results are consistent with the theoretical simulations, verifying the availability of an innovative method for manipulating electromagnetic waves with the merits of higher controllability for dynamic behavior and greater flexibility in the design process.

Simulations of vertical jet penetration using a filtered two-fluid model in a gas-solid fluidized bed

The influence of a vertical jet located at the distributor in a cylindrical fluidized bed on the flow behavior of gas and particles was predicted using a filtered two-fluid model proposed by Sundaresan and coworkers. The distributions of volume fraction and the velocity of particles along the lateral direction were investigated for different jet velocities by analyzing the simulated results. The vertical jet penetration lengths at the different gas jet velocities have been obtained and compared with predictions derived from empirical correlations; the predicted air jet penetration length is discussed. Agreement between the numerical simulations and experimental results has been achieved.

Flexible organic artificial synapse with ultrashort-term plasticity for tunable time-frequency signal processing

A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution method.When combined with a chitosan film,this OAS can achieve an ultrashort-term retention time of only 49 ms for instant electricalcomputing applications;this is the shortest retention time yet achieved by a two-terminal artificial synapse.An array of these flexible OASs can withstand a high bending strain of 5%for 10^(4) cycles;this deformation endurance is a new record.The OAS was also sensitive to the number and frequency of electrical inputs;a tunable cut-off frequency enables dynamic filtering for use in image detail enhancement.This work provides a new resource for development of future neuromorphic computing devices。

Optimizing the Perceptual Quality of Time-Domain Speech Enhancement with Reinforcement Learning

In neural speech enhancement,a mismatch exists between the training objective,i.e.,Mean-Square Error(MSE),and perceptual quality evaluation metrics,i.e.,perceptual evaluation of speech quality and short-time objective intelligibility.We propose a novel reinforcement learning algorithm and network architecture,which incorporate a non-differentiable perceptual quality evaluation metric into the objective function using a dynamic filter module.Unlike the traditional dynamic filter implementation that directly generates a convolution kernel,we use a filter generation agent to predict the probability density function of a multivariate Gaussian distribution,from which we sample the convolution kernel.Experimental results show that the proposed reinforcement learning method clearly improves the perceptual quality over other supervised learning methods with the MSE objective function.

Treating wastewater under zero waste principle using wetland mesocosms

The present investigation demonstrates the synergistic action of using a sedimentation unit together with Cyperus papyrus(L.)wetland enriched with zeolite mineral in one-year round experiment for treating wastewater.The system was designed to support a horizontal surface flow pattern and showed satisfactory removal efficiencies for both physicochemical and bacteriological contaminants within 3 days of residence time.The removal efficiencies ranged between 76.3%and 98.8%for total suspended solids,turbidity,iron,biological oxygen demand,and ammonia.The bacterial indicators(total and fecal coliforms,as well as fecal streptococci)and the potential pathogens(Escherichia coli,Staphylococcus aureus,and Pseudomonas aeruginosa)showed removal efficiencies ranged between 96.9%and 99.8%.We expect the system to offer a smart management for every component according to zero waste principle.The treated effluent was reused to irrigate the landscape of pilot area,and the excess sludge was recycled as fertilizer and soil conditioner.The zeolite mineral did not require regeneration for almost 36 weeks of operation,and enhanced the density of shoots(14.11%)and the height of shoots(15.88%).The harvested plant biomass could be a profitable resource for potent antibacterial and antioxidant bioactive compounds.This could certainly offset part of the operation and maintenance costs and optimize the system implementation feasibility.Although the experiment was designed under local conditions,its results could provide insights to upgrade and optimize the performance of other analogous large-scale constructed wetlands.