An Approach to Assembly Sequence Plannning Based on Hierarchical Strategy and Genetic Algorithm

Using group and subassembly cluster methods, the hierarchical structure of a product is ?generated automatically, which largely reduces the complexity of planning. Based on genetic algorithm, the optimal of assembly sequence of each structure level can be obtained by sequence-by-sequence search. As a result, a better assembly sequence of the product can be generated by combining the assembly sequences of all hierarchical structures, which provides more parallelism and flexibility for assembly operations. An industrial example is solved by this new approach.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

A Hierarchical Reconfiguration Strategy for Bus-Based Multiprocessors

A method of providing redundancy to a class of bus-based multiprocessor arrays is discussed.The reconfiguration is hierarchical,providing global spare replacement at the array level and local reconfiguration within the spare block.Results of yield analysis performed on a 32 processor array are pres- ented.

Hierarchical MPS-Based Three-Dimensional Geological Structure Reconstruction with Two-Dimensional Image(s)

Multiple-point statistics(MPS)is a useful approach to reconstruct three-dimensional models in the macroscopic or microscopic field.Extracting spatial features for three-dimensional reconstruction from two-dimensional training images(TIs),and characterizing non-stationary features with directional ductility are two key issues in MPS simulation.This study presents a step-wise MPS-based three-dimensional structures reconstruction algorithm with the sequential process and hierarchical strategy based on two-dimensional images.An extension method is proposed to construct three-dimensional TIs.With a sequential simulation process,an initial guess at the coarsest scale is simulated,in which hierarchical strategy is used according to the characteristics of TIs.To obtain a more refined realization,an expectation-maximization like iterative process with global optimization is implemented.A concrete example of chondrite micro-structure simulation,in which one scanning electron microscopy(SEM)image of the Heyetang meteorite is used as TI,shows that the presented algorithm can simulate complex non-stationary structures.

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.