Digital holography (DH) stands as a robust imaging method extensively utilized for 3-D imaging. Despite its versatility, when employed in 3-D particle imaging, DH encounters limitations pertaining to resolution and particle concentration. Conventional holographic particle imaging relies on a simplified linearized model and iterative optimization techniques to address pseudoinverse problems. However, this linear assumption inadequately mirrors the complexities of the actual imaging system, resulting in inaccurate particle reconstruction and constrained density. To address these challenges, we introduce an innovative technique named 3-D differentiable holography, which combines a precise theoretical holography model with real-world factors using differentiable programming. This allows us to solve particle imaging problems more effectively using automatic differentiation technology. The effectiveness of our approach is confirmed through numerical simulations and optical experiments, which clearly demonstrate improved imaging accuracy and particle density.

Snow cover in cold and arid regions is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Interferometric synthetic aperture radar (InSAR) technology offers the ability to monitor snow cover in all weather. In this letter, a support vector machine (SVM) method for extracting snow cover based on SAR and optical data in rugged mountain terrain is introduced. In this method, RadarSat-2 InSAR interferometric coherence images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” The analysis results indicate that the coherence in copolarizations is clearly correlated with the underlying surface type and local incidence angle. These two factors, combined with training samples from GF-1 wide field viewer data, were used to build an SVM to classify coherence images in HH polarization. The classification results demonstrate that snow cover extraction using this method can achieve mean accuracies of 83.8% and 77.5% in areas with low and high vegetation coverage, respectively. These accuracies are significantly higher than those achieved by the typical thresholding algorithm (72.7% and 69.2%, respectively).

Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed.

In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept “aging” in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.

Accelerating the convergence of Genetic Algorithms (GAs) is a significant and promising research direction of evolutionary computation. In this paper, a novel Index-based GA (termed IndexGA) is proposed for the acceleration of convergence by reducing the number of fitness evaluations (FEs) in the reproduction procedure, i.e. the process of crossover and mutation. The algorithm divides the solution space into multiple regions, each represented by a unique index. Individuals in the IndexGA are redefined as indexes instead of solutions. In the reproduction procedure, an evaluated region is never evaluated again, and the fitness is directly obtained from the memory. Moreover, to improve the fitness of the promising regions, the algorithm performs an orthogonal local search (OLS) operator on the best-so-far region in each generation. Numerical experiments have been conducted on 13 benchmark functions and an application problem of power electronic circuit (PEC) to investigate the performance of IndexGA. The results show that the index-based strategy and the OLS in IndexGA significantly enhance the performance of GAs in terms of both convergence rate and solution accuracy.

Decisions for admission scheduling in hospitals are a class of optimization problems constrained by many factors. Instead of scheduling the admission of patients directly, this paper proposes a genetic algorithm (GA) designed for the optimization of a long-term admission strategy for the ophthalmology department in hospitals. For the optimization of admission strategy, we devise a coding scheme of strategies and define the objective functions for two objectives: efficiency and fairness. The proposed algorithm utilizes historical data of the hospital for evaluation of chromosomes. Experiments are conducted on several cases, and the strategy optimized by the proposed GA is compared with the first come first serve (FCFS) strategy and the greedy strategy. Experimental results show that strategies optimized by the proposed algorithm outperform FCFS and the greedy strategy.

Since the tracking error does not truly reflect product quality, the contouring error is introduced in the dynamic control of parallel machining systems. For real-time computation reason, the contouring error is approximated by the distance from the actual position to the tangent plane of the desired contour at the corresponding desired position, i.e., the error in normal direction. By attaching a moving task frame to each point on a desired trajectory, the tracking error is decomposed into tangential error and normal error. By the transformation introduced by the task frame, we obtain error dynamics in the task frame. The error dynamics is decoupled into error dynamics in tangential and normal directions by applying the computed torque control and choosing appropriate system matrices. Simulation shows that a larger bandwidth of the normal dynamics leads to smaller contouring error given fixed natural frequency for the tangential dynamics. By a comparison with the PD control in the world frame, the task space based contouring control exhibits much better performance in contouring accuracy

In this paper, the desired performance of the mechanical system is specified in terms of contouring error instead of traditional method which specifies a task as a desired timed trajectory tracking problem. By defining the task frame, a simplified contouring error model is obtained through projecting tracking error to this new frame. Then a novel adaptive contouring controller is developed directly in the task frame to handle bounded external disturbances and system model uncertainties while maintaining superior contouring tracking performance. The algorithm effectively exploit the the structure of manipulator dynamics to reduce the computation complexity. Experimental results on an AC motor driven X-Y table demonstrate the merit of significant improvement of the proposed controller for increasing contouring accuracy compared with other conventional control algorithms.

Kinematic design of parallel manipulators is addressed in this paper. By observation that regular (e.g., hyper-rectangular) workspaces are desirable for most machines, we propose the concept of effective regular workspace, which reflects both requirements on the workspace shape and quality. Dexterity index is utilized to characterize the effectiveness of the workspace. The optimal design problem is then formulated to find a manipulator geometry that maximizes the effective regular workspace. Since the optimal design problem is a constrained nonlinear optimization problem without explicit analytical expressions, the controlled random search (CRS) technique, which was reported robust and reliable, is applied to numerically solve the problem. The commonly-used Stewart-Gough platform is employed as an example to demonstrate the design procedure.

In the contouring control, the trajectory and the tolerance information are specified in the task space. Based on this observation we propose to design the controller in the task space directly. First, by defining the projection map on the cotangent space of the mechanical system the equation of motion is derived in the task space. Then a novel contouring controller is got based on the geometric control theory. The controller is transferred into the joint space for implementation. The simulation results show the performance of the controller.