Investigation of Communication Constraints in Distributed Multi-Agent Systems

Based on a simple flocking model with collision avoidance, a set of investigations of multi-agent system communication constraints have been conducted, including distributed estimation of global features, the influence of jamming, and communication performance optimization. In flocking control, it is necessary to achieve a common velocity among agents and maintain a safe distance between neighboring agents. The local information among agents is exchanged in a distributed fashion to help achieve velocity consensus. A distributed estimation algorithm was recently proposed to estimate the group’s global features based on achieving consensus among agents’ local estimations of such global features. To reduce the communication load, the exchange of local estimations among agents occurs at discrete time instants defined by an event-triggering mechanism. To confirm the effectiveness of the new distributed estimation algorithm, we simulated the algorithm while adopting a simple flocking control technique with collision avoidance. In addition, the effect of jamming on flocking control and the distributed algorithm is studied through computer simulations. Finally, to better exploit the communication channel among agents, we study a recently proposed formation control multi-agent algorithm, which optimizes the inter-agent distance in order to achieve optimum inter-agent communication performance. The study is also conducted through computer simulations, which confirms the effectiveness of the algorithm

WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection

We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations. Predominant works integrate region proposal mechanisms with convolutional neural networks (CNN). Although CNN is proficient in extracting discriminative local features, grand challenges still exist to measure the likelihood of a bounding box containing a complete object (i.e., "objectness"). In this paper, we propose a novel WSOD framework with Objectness Distillation (i.e., WSOD^2) by designing a tailored training mechanism for weakly-supervised object detection. Multiple regression targets are specifically determined by jointly considering bottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptive linear combination. As bounding box regression can facilitate a region proposal learning to approach its regression target with high objectness during training, deep objectness representation learned from bottom-up evidences can be gradually distilled into CNN by optimization. We explore different adaptive training curves for BU/TD objectness, and show that the proposed WSOD^2 can achieve state-of-the-art results.Comment: Accepted as a ICCV 2019 poster pape

Loss-difference-induced localization in a non-Hermitian honeycomb photonic lattice

Non-Hermitian systems with complex-valued energy spectra provide an extraordinary platform for manipulating unconventional dynamics of light. Here, we demonstrate the localization of light in an instantaneously reconfigurable non-Hermitian honeycomb photonic lattice that is established in a coherently-prepared atomic system. One set of the sublattices is optically modulated to introduce the absorptive difference between neighboring lattice sites, where the Dirac points in reciprocal space are extended into dispersionless local flat bands. When these local flat bands are broad enough due to larger loss difference, the incident beam is effectively localized at one set of the lattices with weaker absorption, namely, the commonly seen power exchange between adjacent channels in photonic lattices is effectively prohibited. The current work unlocks a new capability from non-Hermitian two-dimensional photonic lattices and provides an alternative route for engineering tunable local flat bands in photonic structures

Indirect coupling method for structural analysis of refuge chamber

Structural analysis is important in the design of a refuge chamber, which can ensure the structural safety of the refuge chamber in case of an explosion. In this paper, an indirect coupling method is utilized to calculate deformation of a refuge chamber under explosion, when gas explosion is simulated in a roadway model, and the pressure waves on different locations of chamber are extracted. The extracted pressure-time curves are applied to a detailed model of the refuge chamber to obtain deformation values. However, reliabilities and validities of the simulation results are not provided. Thereby, we conducted three groups of small-scale physical experiments for comparing the corresponding simulation results calculated by the indirect coupling method. Meanwhile, the theoretical values were obtained by the method of extracting the specific impulse. The results show that the simulation values fit well with the experimental and theoretical values. The process of applying a pressure-time curve to the model covers the specific impulse which acts on the prototype. This method can be used to calculate the deformation of complex equipment under explosion