An elliptical cover problem in drone delivery network design and its solution algorithms

Given n demand points in a geographic area, the elliptical cover problem is to determine the location of p depots (anywhere in the area) so as to minimize the maximum distance of an economical delivery trip in which a delivery vehicle starts from the nearest depot to a demand point, visits the demand point and then returns to the second nearest depot to that demand point. We show that this problem is NP-hard, and adapt Cooper’s alternating locate-allocate heuristic to find locally optimal solutions for both the point-coverage and area-coverage scenarios. Experiments show that most locally optimal solutions perform similarly well, suggesting their sufficiency for practical use. The one-dimensional variant of the problem, in which the service area is reduced to a line segment, permits recursive algorithms that are more efficient than mathematical optimization approaches in practical cases. The solution also provides the best-known lower bound for the original problem at a negligible computational cost

Super-directivity formation and numerical analysis of acoustic array

The formation of super-directivity of an acoustic array is firstly analyzed to construct a general mathematical model of the array with super-directivity and maximum signal-to-noise ratio (SNR). Then the numerical simulation on the super-directivity of the array is carried out for the arrays with different shapes, element number and apertures. It shows that, circular array with regular shape and Archimedean spiral array with irregular shape have optimum directivity

The Performances Optimization of Finger Seal Based on Fuzzy Game Theory

AbstractLeakage and abrasion are two key performances of finger seals (FS). They not only contradict each other in FS design but also relate to many design parameters. Moreover, in the multi-objective optimization progress, the problems of optimizing results decision and preference requirement for optimization objectives are still challenge to researcher. So far, they are still important influence factors for advanced FS design. Therefore, the current work presents a new multi-objective optimization method by introducing game theory and fuzzy comprehensive evaluation theory. The optimizing results are compared to that of the general optimization method and finite element method (FEM). The study show that the FS, which is obtained by presented optimization method, has good performances. Compared respectively with the general optimization method and FEM, the computational results indicate that the presented method can effectively reflect the different response requirements of optimization objectives. Furthermore, the decision-making difficulty for multi-objective optimization of FS performances is significantly reduced

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in Multi-Agent RL

Most existing works consider direct perturbations of victim's state/action or the underlying transition dynamics to show vulnerability of reinforcement learning agents under adversarial attacks. However, such direct manipulation may not always be feasible in practice. In this paper, we consider another common and realistic attack setup: in a multi-agent RL setting with well-trained agents, during deployment time, the victim agent $\nu$ is exploited by an attacker who controls another agent $\alpha$ to act adversarially against the victim using an \textit{adversarial policy}. Prior attack models under such setup do not consider that the attacker can confront resistance and thus can only take partial control of the agent $\alpha$, as well as introducing perceivable ``abnormal'' behaviors that are easily detectable. A provable defense against these adversarial policies is also lacking. To resolve these issues, we introduce a more general attack formulation that models to what extent the adversary is able to control the agent to produce the adversarial policy. Based on such a generalized attack framework, the attacker can also regulate the state distribution shift caused by the attack through an attack budget, and thus produce stealthy adversarial policies that can exploit the victim agent. Furthermore, we provide the first provably robust defenses with convergence guarantee to the most robust victim policy via adversarial training with timescale separation, in sharp contrast to adversarial training in supervised learning which may only provide {\it empirical} defenses

Elements content in tree rings from Xi'an, China and environmental variations in the past 30 years

Using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES), the characteristics of chemical elements were analyzed in white poplar (Populus bonatii Levl.) and ailanthus (Ailanthus altissima (Mill.) Swingle) from three sites in the town of Xi'an, China. The results indicated that the concentration variations of Pb and Cd in tree rings were consistent with that of the environment where the trees were growing. P and Zn were translocated within tree rings to a certain degree, which led to an inaccurate pollution reconstruction. We also found that white poplar had a stronger absorptive capacity of Cd and Zn than ailanthus, which could make white poplar better as a species in environmental remediation. From this research we can see the great potential of tree rings for studying the history of different element pollution in the environment, showing that dendrochemical methods could be used as a powerful component in environmental monitoring programmes, to reconstruct past pollution history at the time when monitoring systems were not yet installed. (c) 2017 Elsevier B.V. All rights reserved