Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data,traditional regression models cannot deal with uncertain big data efficiently and accurately.In order to make up for this deficiency,this paper proposes a quantum fuzzy regression model,which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation.In this paper,data envelopment analysis(DEA)is used to calculate the degree of importance of each data point.Meanwhile,Harrow,Hassidim and Lloyd(HHL)algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation.The application of the quantum fuzzy regression model to smallscale financial data proves that its accuracy is greatly improved compared with the quantum regression model.Moreover,due to the introduction of quantum computing,the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model.The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data.Thus,it is a new model for efficient and accurate big data processing in uncertain environments.

Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment： i） uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii） stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it environment with complex characteristics. Finally, numerical robot manipulator. also shows robustness of the proposed controller to uncertain simulation verifies results via contact task of a two rigid-links

Bayesian Tracking in an Uncertain Shallow Water Environment

A Bayesian source tracking approach is developed to track a moving acoustic source in an uncertain ocean environment. This approach treats the environmental parameters （e.g., water depth, sediment and bottom parameters） at the source location and the source parameters （e.g., source depth, range and speed） as unknown random variables that evolve as the source moves. To track a target with low signal-to-noise ratio （SNR）, acoustic signals from a series of observations are treated in a simultaneous inversion. This allows real-time updating of the environment and accurate tracking of the moving source. The noise signals radiated from a surface ship target are processed and analyzed. It is found that the Bayesian source tracking method could enhance the localization accuracy in an uncertain water environment and low SNR.

Deep Reinforcement Learning Based Decision‑Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments

Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.

Robust Airfoil Optimization with Multi-objective Estimation of Distribution Algorithm

A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Mach numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Henne shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is constructed with those points to reduce computing costs. Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.

Local minima-free design of artificial coordinating fields

In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.

Comparison of two Bayesian-point-estimation methods in multiple-source localization

Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.

Motion-copying method with symbol sequence-based phase switch control for intelligent optical manufacturing

Implementation of robot-based motion control in optical machining demonstrably enhances the machining quality.The introduction of motion-copying method enables learning and replicating manipulation from experienced technicians.Nevertheless,the location uncertainties of objects and frequent switching of manipulated spaces in practical applications impose constraints on their further advancement.To address this issue,a motion-copying system with a symbol-sequence-based phase switch control(SSPSC)scheme was developed by transferring the operating skills and intelligence of technicians to mechanisms.The manipulation process is decomposed,symbolised,rearranged,and reproduced according to the manufacturing characteristics regardless of the change in object location.A force-sensorless adaptive sliding-mode-assisted reaction force observer(ASMARFOB),wherein a novel dual-layer adaptive law was designed for high-performance fine force sensing,was established.The uniformly ultimate boundedness(UUB)of the ASMARFOB is guaranteed based on the Lyapunov stability theory,and the switching stability of the SSPSC was examined.Validation simulations and experiments demonstrated that the proposed method enables better motion reproduction with high consistency and adaptability.The findings of this study can provide effective theoretical and practical guidance for high-precision intelligent optical manufacturing.

Robust time reversal processing for active detection of a small bottom target in a shallow water waveguide

With the spatial-temporal focusing of acoustic energy,time reversal processing(TRP) shows the potential application for active target detection in shallow water.To turn the ‘potential’ into a reality,the TRP based on a model source(MS) instead of a physical probe source(PS) is investigated.For uncertain ocean environments,the robustness of TRP is discussed for the narrowband and broadband signal respectively.The channel transfer function matrix is first constructed in the acoustic perturbation space.Then a steering vector for time reversal transmission is obtained by singular value decomposition(SVD) of the matrix.For verification of the robust TRP,the tank experiments of time reversal transmission focusing and its application for active target detection are undertaken.The experimental results have shown that the robust TRP can effectively detect and locate a small bottom target.