Vibration characteristics analysis of CLD/plate based on the multi-objective optimization

The multi-objective optimization configurations of thickness, the locations of constrained layer damping (CLD) patches for plate are investigated and the vibration characteristics of the CLD/plate are analyzed based on the Pareto optimal solutions. The finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the plate with CLD treatments to predict its vibration characteristics. A multi-objective optimization model for CLD/plate is formulated based on the dynamical equation. The design objectives are to maximize the mode loss factors, while the design variables include the thicknesses of viscoelastic material (VEM) and constrained layer material (CLM), the locations of CLD treatments on the plate. Aiming to the special real-integer hybrid variables optimization problems, the non-dominated sorting genetic algorithm II (NSGA-II) is employed and improved. Two different optimization strategies are proposed. As the results of the numerical example, the various feasible Pareto optimal solutions are successfully obtained, and effects of the design variables on the vibration characteristics are discussed. The influences of algorithm parameters on the optimization procedure are also investigated. The results show the validity of improved NSGA-II and the optimization strategies. The potential multiple selections of CLD treatments for different vibration control objectives and constrained conditions are also demonstrated

Identification and Control of Nonlinear Singularly Perturbed Systems Using Multi-time-scale Neural Networks

Many industrial systems are nonlinear with "slow" and "fast" dynamics because of the presence of some ``parasitic" parameters such as small time constants, resistances, inductances, capacitances, masses and moments of inertia. These systems are usually labeled as "singularly perturbed" or ``multi-time-scale" systems. Singular perturbation theory has been proved to be a useful tool to control and analyze singularly perturbed systems if the full knowledge of the system model parameters is available. However, the accurate and faithful mathematical models of those systems are usually difficult to obtain due to the uncertainties and nonlinearities. To obtain the accurate system models, in this research, a new identification scheme for the discrete time nonlinear singularly perturbed systems using multi-time-scale neural network and optimal bounded ellipsoid method is proposed firstly. Compared with other gradient descent based identification schemes, the new identification method proposed in this research can achieve faster convergence and higher accuracy due to the adaptively adjusted learning gain. Later, the optimal bounded ellipsoid based identification method for discrete time systems is extended to the identification of continuous singularly perturbed systems. Subsequently, by adding two additional terms in the weight's updating laws, a modified identification scheme is proposed to guarantee the effectiveness of the identification algorithm during the whole identification process. Lastly, through introducing some filtered variables, a robust neural network training algorithm is proposed for the system identification problem subjected to measurement noises. Based on the identification results, the singular perturbation theory is introduced to decompose a high order multi-time-scale system into two low order subsystems -- the reduced slow subsystem and the reduced fast subsystem. Then, two controllers are designed for the two subsystems separately. By using the singular perturbation theory, an adaptive controller for a regulation problem is designed in this research firstly. Because the system order is reduced, the adaptive controller proposed in this research has a simpler structure and requires much less computational resources, compared with other conventional controllers. Afterward, an indirect adaptive controller is proposed for solving the trajectory tracking problem. The stability of both identification and control schemes are analyzed through the Lyapunov approach, and the effectiveness of the identification and control algorithms are demonstrated using simulations and experiments

Spectroscopic Confirmation of two Extremely Massive Protoclusters BOSS1244 and BOSS1542 at z=2.24z=2.24

We present spectroscopic confirmation of two new massive galaxy protoclusters at $z=2.24\pm0.02$, BOSS1244 and BOSS1542, traced by groups of Coherently Strong Ly$\alpha$ Absorption (CoSLA) systems imprinted in the absorption spectra of a number of quasars from the SDSS III and identified as overdensities of narrowband-selected H$\alpha$ emitters (HAEs). Using MMT/MMIRS and LBT/LUCI near-infrared (NIR) spectroscopy, we confirm 46 and 36 HAEs in the BOSS1244 and BOSS1542 fields, respectively. BOSS1244 displays a South-West (SW) component at $z=2.230\pm0.002$ and another North-East (NE) component at $z=2.246\pm0.001$ with the line-of-sight velocity dispersions of $405\pm202$ km s$^{-1}$ and $377\pm99$ km s$^{-1}$, respectively. Interestingly, we find that the SW region of BOSS1244 contains two substructures in redshift space, likely merging to form a larger system. In contrast, BOSS1542 exhibits an extended filamentary structure with a low velocity dispersion of $247\pm32$ km s$^{-1}$ at $z=2.241\pm0.001$, providing a direct confirmation of a large-scale cosmic web in the early Universe. The galaxy overdensities $\delta_{\rm g}$ on the scale of 15 cMpc are $22.9\pm4.9$, $10.9\pm2.5$, and $20.5\pm3.9$ for the BOSS1244 SW, BOSS1244 NE, and BOSS1542 filament, respectively. They are the most overdense galaxy protoclusters ($\delta_{\rm g}>20$) discovered to date at $z>2$. These systems are expected to become virialized at $z\sim0$ with a total mass of $M_{\rm SW}=(1.59\pm0.20)\times10^{15}$ $M_{\odot}$, $M_{\rm NE} =(0.83\pm0.11)\times10^{15}$ $M_{\odot}$ and $M_{\rm filament}=(1.42\pm0.18)\times10^{15}$ $M_{\odot}$, respectively. Together with BOSS1441 described in Cai et al. (2017a), these extremely massive overdensities at $z=2-3$ exhibit different morphologies, indicating that they are in different assembly stages in the formation of early galaxy clusters.Comment: 28 pages, 13 figures, 6 tables, accepted for publication in ApJ. The complete Abstract is presented in the manuscrip

Vibration characteristics analysis of CLD/plate based on the multi-objective optimization

The multi-objective optimization configurations of thickness, the locations of constrained layer damping (CLD) patches for plate are investigated and the vibration characteristics of the CLD/plate are analyzed based on the Pareto optimal solutions. The finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the plate with CLD treatments to predict its vibration characteristics. A multi-objective optimization model for CLD/plate is formulated based on the dynamical equation. The design objectives are to maximize the mode loss factors, while the design variables include the thicknesses of viscoelastic material (VEM) and constrained layer material (CLM), the locations of CLD treatments on the plate. Aiming to the special real-integer hybrid variables optimization problems, the non-dominated sorting genetic algorithm II (NSGA-II) is employed and improved. Two different optimization strategies are proposed. As the results of the numerical example, the various feasible Pareto optimal solutions are successfully obtained, and effects of the design variables on the vibration characteristics are discussed. The influences of algorithm parameters on the optimization procedure are also investigated. The results show the validity of improved NSGA-II and the optimization strategies. The potential multiple selections of CLD treatments for different vibration control objectives and constrained conditions are also demonstrated

Deep Imaging of the HCG 95 Field.I.Ultra-diffuse Galaxies

We present a detection of 89 candidates of ultra-diffuse galaxies (UDGs) in a 4.9 degree$^2$ field centered on the Hickson Compact Group 95 (HCG 95) using deep $g$- and $r$-band images taken with the Chinese Near Object Survey Telescope. This field contains one rich galaxy cluster (Abell 2588 at $z$=0.199) and two poor clusters (Pegasus I at $z$=0.013 and Pegasus II at $z$=0.040). The 89 candidates are likely associated with the two poor clusters, giving about 50 $-$ 60 true UDGs with a half-light radius $r_{\rm e} > 1.5$ kpc and a central surface brightness $\mu(g,0) > 24.0$ mag arcsec$^{-2}$. Deep $z$'-band images are available for 84 of the 89 galaxies from the Dark Energy Camera Legacy Survey (DECaLS), confirming that these galaxies have an extremely low central surface brightness. Moreover, our UDG candidates are spread over a wide range in $g-r$ color, and $\sim$26% are as blue as normal star-forming galaxies, which is suggestive of young UDGs that are still in formation. Interestingly, we find that one UDG linked with HCG 95 is a gas-rich galaxy with H I mass $1.1 \times 10^{9} M_{\odot}$ detected by the Very Large Array, and has a stellar mass of $M_\star \sim 1.8 \times 10^{8}$ $M_{\odot}$. This indicates that UDGs at least partially overlap with the population of nearly dark galaxies found in deep H I surveys. Our results show that the high abundance of blue UDGs in the HCG 95 field is favored by the environment of poor galaxy clusters residing in H I-rich large-scale structures.Comment: Published in Ap

ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended

Clustering framework to identify traffic conflicts and determine thresholds based on trajectory data

Traffic conflict indicators are essential for evaluating traffic safety and analyzing trajectory data, especially in the absence of crash data. Previous studies have used traffic conflict indicators to predict and identify conflicts, including time-to-collision (TTC), proportion of stopping distance (PSD), and deceleration rate to avoid a crash (DRAC). However, limited research is conducted to understand how to set thresholds for these indicators while accounting for traffic flow characteristics at different traffic states. This paper proposes a clustering framework for determining surrogate safety measures (SSM) thresholds and identifying traffic conflicts in different traffic states using high-resolution trajectory data from the Citysim dataset. In this study, unsupervised clustering is employed to identify different traffic states and their transitions under a three-phase theory framework. The resulting clusters can then be utilized in conjunction with surrogate safety measures (SSM) to identify traffic conflicts and assess safety performance in each traffic state. From different perspectives of time, space, and deceleration, we chose three compatible conflict indicators: TTC, DRAC, and PSD, considering functional differences and empirical correlations of different SSMs. A total of three models were chosen by learning these indicators to identify traffic conflict and non-conflict clusters. It is observed that Mclust outperforms the other two. The results show that the distribution of traffic conflicts varies significantly across traffic states. A wide moving jam (J) is found to be the phase with largest amount of conflicts, followed by synchronized flow phase (S) and free flow phase(F). Meanwhile, conflict risk and thresholds exhibit similar levels across transitional states

TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety

Large Language Models (LLMs) have shown remarkable effectiveness in various general-domain natural language processing (NLP) tasks. However, their performance in transportation safety domain tasks has been suboptimal, primarily attributed to the requirement for specialized transportation safety expertise in generating accurate responses [1]. To address this challenge, we introduce TrafficSafetyGPT, a novel LLAMA-based model, which has undergone supervised fine-tuning using TrafficSafety-2K dataset which has human labels from government produced guiding books and ChatGPT-generated instruction-output pairs. Our proposed TrafficSafetyGPT model and TrafficSafety-2K train dataset are accessible at https://github.com/ozheng1993/TrafficSafetyGPT

The effect of the support on the surface composition of PtCu alloy nanocatalysts: In situ XPS and HS-LEIS studies

Pt是一类高效、稳定的催化剂, 但Pt资源短缺且价格昂贵, 限制了其广泛商业化应用. 合金化可以使Pt的用量大为减少,; 且往往能显著提高其催化性能, 因而广泛应用于多相催化和电催化. 其中PtCu合金是一类很有前景的催化剂, Cu资源丰富、价格低廉,; 不仅降低了成本, 而且由于合金效应提高了催化剂的活性和稳定性. 由于合金的粒径、形状、组成及结构是影响其催化性能的重要因素,; 目前研究大多关注这些特征的可控合成.然而, 大多工业金属催化剂都是负载于氧化物上以提高催化性能,; 合金纳米粒子的形貌以及表面组成因与载体作用而发生改变, 也就是所谓的载体效应. 这体现在金属/氧化物界面处,; 能够促进金属粒子分散、改变其形貌甚至化学态、进而改变其催化性能, 其中最具代表性的金属-载体强相互作用. 因此,; 研究不同氧化物载体上合金催化剂的分散度、表面组成、化学态, 特别是不同气氛的影响, 对明确影响催化剂性能的关键控制因素非常重要.; 但是由于多相催化剂的复杂性, 且表面灵敏的测试手段很少, 目前相关报道还不多.; 近年发展起来的高灵敏度低能离子散射谱(HS-LEIS)是表面层灵敏的测试技术,; 可以测定最表面层的组成和含量.本文采用溶剂热共还原法成功制备了均一单相、粒径分布较窄的PtCu_x合金纳米颗粒,; 并运用浸渍法将其负载在TiO_2载体上, 以保证载体上纳米粒子组成的均一性.; 应用准原位X-射线光电子能谱(XPS)和高HS-LEIS对负载的PtCu合金纳米催化剂在不同条件处理后的表面组成和化学状态进行表征,; 发现催化剂的表面组成、分布、形貌和化学状态显著受到载体和处理条件的影响, 同时得到负载和未负载的催化剂表面组成与体相组成关系的相图. 结果表明,; PtCu_x/TiO_2催化剂在连续氧化过程中, Cu被氧化并较好在载体表面铺展, Pt-Cu合金状态被破坏,; Pt可能主要形成单一金属的纳米粒子, 并在界面处形成Ptd+. 在连续还原过程中, 部分被还原的Cu, 与Pt形成富Pt合金粒子.; 催化剂表面层主要是Cu, Pt很少, 与体相组成有很大差别, 说明载体对Cu的分散起到重要作用.Supported PtCu alloys have been broadly applied in heterogeneous catalysis and electrocatalysis owing to their excellent catalytic performance and high CO tolerance. It is important to analyze the outermost surface composition of the supported alloy nanoparticles to understand the nature of the catalytically active sites. In this paper, homogeneous face-centered cubic PtCu nanoparticles with a narrow particle size distribution were successfully fabricated and dispersed on a high-surface-area TiO2 powder support. The samples were oxidized and reduced in situ and then introduced into the ultrahigh vacuum chamber to measure the topmost surface composition by high-sensitivity low-energy ion scattering spectroscopy, and to determine the oxidation states of the elements by X-ray photoelectron spectroscopy. The surface composition and morphology, elemental distribution, and oxidation states of the components were found to be significantly affected by the support and treatment conditions. The PtCu is de-alloyed upon oxidation with CuO wetting on the TiO2 surface and re-alloyed upon reduction. Phase diagrams of the surface composition and the bulk composition were plotted and compared for the supported and unsupported materials. (C) 2017, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.National Basic Research Program of China (973 Program) [2013CB933102];; National Natural Science Foundation of China [21273178, 21573180,; 91545204]; Xiamen-Zhuoyue Biomass Energy Co. Ltd