Hopf bifurcation and optimal control in a diffusive predator-prey system with time delay and prey harvesting

In this paper, we investigated the dynamics of a diffusive delayed predator-prey system with Holling type II functional response and nozero constant prey harvesting on no-flux boundary condition. At first, we obtain the existence and the stability of the equilibria by analyzing the distribution of the roots of associated characteristic equation. Using the time delay as the bifurcation parameter and the harvesting term as the control parameter, we get the existence and the stability of Hopf bifurcation at the positive constant steady state. Applying the normal form theory and the center manifold argument for partial functional differential equations, we derive an explicit formula for determining the direction and the stability of Hopf bifurcation. Finally, an optimal control problem has been considered

Multimodal Speech Recognition for Language-Guided Embodied Agents

Benchmarks for language-guided embodied agents typically assume text-based instructions, but deployed agents will encounter spoken instructions. While Automatic Speech Recognition (ASR) models can bridge the input gap, erroneous ASR transcripts can hurt the agents' ability to complete tasks. In this work, we propose training a multimodal ASR model to reduce errors in transcribing spoken instructions by considering the accompanying visual context. We train our model on a dataset of spoken instructions, synthesized from the ALFRED task completion dataset, where we simulate acoustic noise by systematically masking spoken words. We find that utilizing visual observations facilitates masked word recovery, with multimodal ASR models recovering up to 30% more masked words than unimodal baselines. We also find that a text-trained embodied agent successfully completes tasks more often by following transcribed instructions from multimodal ASR models. github.com/Cylumn/embodied-multimodal-asrComment: 5 pages, 5 figures, 24th ISCA Interspeech Conference (INTERSPEECH 2023

Aerodynamic Parameter Estimation of a Symmetric Projectile Using Adaptive Chaotic Mutation Particle Swarm Optimization

This article details a new optimizing algorithm called Adaptive Chaotic Mutation Particle Swarm Optimization (ACM-PSO). The new algorithm is used to perform aerodynamic parameter estimation on a spinning symmetric projectile. The main creative ideas of this new algorithm are as follows. First, a self-adaptive weight function is used so that the inertial weight can be adjusted dynamically by itself. Second, the initialized particle is generated by chaos theory. Last, a method that can be used to judge whether the algorithm has fallen into a local optimum is established. The common testing function is used to test the new algorithm, and the result shows that, compared with the basic particle swarm optimization (PSO) algorithm, it is more likely to have a quick convergence and high accuracy and precision, leading to extensive application. Simulated ballistic data are used as testing data, and the data are subjected to the new algorithm to identify the aerodynamic parameters of a spinning symmetric projectile. The result shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering

Perspective of monochromatic gamma-ray line detection with the High Energy cosmic-Radiation Detection (HERD) facility onboard China's Space Station

HERD is the High Energy cosmic-Radiation Detection instrument proposed to operate onboard China's space station in the 2020s. It is designed to detect energetic cosmic ray nuclei, leptons and photons with a high energy resolution ($\sim1\%$ for electrons and photons and $20\%$ for nuclei) and a large geometry factor ($>3\,{ m^2\,sr}$ for electrons and diffuse photons and $>2\,{ m^2\,sr}$ for nuclei). In this work we discuss the capability of HERD to detect monochromatic $\gamma$-ray lines, based on simulations of the detector performance. It is shown that HERD will be one of the most sensitive instruments for monochromatic $\gamma$-ray searches at energies between $\sim10$ to a few hundred GeV. Above hundreds of GeV, Cherenkov telescopes will be more sensitive due to their large effective area. As a specific example, we show that a good portion of the parameter space of a supersymmetric dark matter model can be probed with HERD.Comment: 9 pages, 7 figures, matches version published in Astropart.Phy