Structure modal optimization of a strapdown inertial navigation system for an electric helicopter using an adaptive surrogate model

The purpose of this research is to prove the eventuality of using a novel adaptive surrogate model for optimization problems. The adaptive surrogate model is based on iteration sampling and extended radial basis function (ERBF). This method improves the precision by a means that new sample points is placed in the blank area and all the sample points is uniformly distributed in the design region. The precision of the surrogate model is checked using standard error measure to determine whether updating the surrogate model or not. Since the prediction of modal frequencies require structure modal simulations. In order to decrease the number of computer simulations, a Multi-Island GA approach is combined with the adaptive surrogate model to find the optimum modal frequencies of a strapdown inertial navigation system for electric helicopters. The strapdown inertial navigation system is comprised of damping material, counterweight material and inertial navigation sensor. This is a multi-objective functions optimization problem since the modal frequencies are considered from mode 1 to mode 6 in this paper. Several weights of multi-objective functions are utilized to research the modal frequencies. The whole number of 15 sampling points is picked to build the primary surrogate model using Latin hypercube sampling (LHS). The results of adaptive surrogate model show that two new sampling points are needed to reform the precision of the surrogate model under the condition of 2 % confidence bounds. The structure modal optimization results show that the choice of the weights for the multi-objective functions has a major effect on the final optimum modal frequencies. Time- and frequency-domain analysis indicated that the optimum modal frequencies are far away from the excitation frequencies to avoid strapdown inertial navigation system resonance as far as possible

An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context

One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control. Existing works on adaptation to unknown environment contexts either assume the contexts are the same for the whole episode or assume the context variables are Markovian. However, in many real-world applications, the environment context usually stays stable for a stochastic period and then changes in an abrupt and unpredictable manner within an episode, resulting in a segment structure, which existing works fail to address. To leverage the segment structure of piecewise stable context in real-world applications, in this paper, we propose a \textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented \textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief distribution over latent context with the posterior over segment length and perform more accurate belief context inference with observed data within the current context segment. The inferred belief context can be leveraged to augment the state, leading to a policy that can adapt to abrupt variations in context. We demonstrate empirically that SeCBAD can infer context segment length accurately and outperform existing methods on a toy grid world environment and Mujuco tasks with piecewise-stable context.Comment: NeurIPS 202

Time Sequence Map for Interpreting the Thermal Runaway Mechanism of Lithium-Ion Batteries With LiNixCoyMnzO2 Cathode

Thermal runaway is one of the key failure reasons for the lithium-ion batteries. The potential of thermal runaway in applications increases when the industry starts to use high energy LiNixCoyMnzO2 cathode. The thermal runaway mechanism is still unclear, because the side reactions are complex. Heat generation during thermal runaway can be caused by the decomposition of individual cell components, or by interactive reactions between multiple components. This paper tries to comb the heat sources during thermal runaway using a novel method named the “Time Sequence Map” (TSM). The TSM tracks the heat sources according to the notion of thermodynamic systems. The thermodynamic system means a combination of materials that stay and react together, and generate heat independently without interruptions from other thermodynamic systems. With the help of the defined thermodynamic systems, researchers will be rescued from being trapped in the complex reactions, and the heat sources during thermal runaway can be clearly explained from bottom up. The thermal runaway results for two battery samples demonstrate the validity of the TSM. The TSM shows the heat sources including that: (1) fire, (2) internal short circuit, (3) oxidation-reduction reaction between the cathode and anode, etc. The contributions for the heat sources to the thermal runaway are further discussed. Conclusions come to: (1) the major heat source is the oxidation-reduction reaction; (2) the fire releases lots of heat, but most of the heat is not to heat the cell itself; (3) the internal short circuit is critical to trigger the oxidation-reduction reaction; (4) the internal short circuit is not the major heat source that heat the cell to 800°C or higher; (5) the oxidation-reduction reaction is triggered when the temperature reaches a critical temperature. The TSM helps depict the frontiers in the researches of battery thermal runaway. It suggests that we focus on: (1) the relationship between internal short circuit and thermal runaway; (2) the mechanism of the oxidation-reduction reaction between the cathode and anode; (3) the detailed reaction mechanisms for a specific thermodynamic system within the cell

Phenotypic Plasticity of Staphylococcus aureus in Liquid Medium Containing Vancomycin

Phenotypic plasticity enables individuals to develop different phenotypes in a changing environment and promotes adaptive evolution. Genome-wide association study (GWAS) facilitates the study of the genetic basis of bacterial phenotypes, and provides a new opportunity for bacterial phenotypic plasticity research. To investigate the relationship between growth plasticity and genotype in bacteria, 41 Staphylococcus aureus strains, including 29 vancomycin-intermediate S. aureus (VISA) strains, were inoculated in the absence or presence of vancomycin for 48 h. Growth curves and maximum growth rates revealed that strains with the same minimum inhibitory concentration (MIC) showed different levels of plasticity in response to vancomycin. A bivariate GWAS was performed to map single-nucleotide polymorphisms (SNPs) associated with growth plasticity. In total, 227 SNPs were identified from 14 time points, while 15 high-frequency SNPs were mapped to different annotated genes. The P-values and growth variations between the two cultures suggest that non-coding region (SNP 738836), ebh (SNP 1394043), drug transporter (SNP 264897), and pepV (SNP 1775112) play important roles in the growth plasticity of S. aureus. Our study provides an alternative strategy for dissecting the adaptive growth of S. aureus in vancomycin and highlights the feasibility of bivariate GWAS in bacterial phenotypic plasticity research