Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall

Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly

Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily concentrate on designing lightweight adapters and their interaction with pre-trained models, with the goal of minimizing the number of parameters requiring updates. In this study, we propose a novel Adapter Re-Composing (ARC) strategy that addresses efficient pre-trained model adaptation from a fresh perspective. Our approach considers the reusability of adaptation parameters and introduces a parameter-sharing scheme. Specifically, we leverage symmetric down-/up-projections to construct bottleneck operations, which are shared across layers. By learning low-dimensional re-scaling coefficients, we can effectively re-compose layer-adaptive adapters. This parameter-sharing strategy in adapter design allows us to significantly reduce the number of new parameters while maintaining satisfactory performance, thereby offering a promising approach to compress the adaptation cost. We conduct experiments on 24 downstream image classification tasks using various Vision Transformer variants to evaluate our method. The results demonstrate that our approach achieves compelling transfer learning performance with a reduced parameter count. Our code is available at \href{https://github.com/DavidYanAnDe/ARC}{https://github.com/DavidYanAnDe/ARC}.Comment: Paper is accepted to NeurIPS 202

Introduction To \u27Artificial Intelligence In Failure Analysis Of Transportation Infrastructure And Materials\u27

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyze the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyze the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multiscale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc

Using Video to Validate Vehicle Speed Uncertainty in Vertical Side Collisions

Vehicle speed access is an important part of road traffic accidents. Many factors affect the speed of the vehicle in vertical side collisions. Uncertainty in speed calculations related to vehicle collision was researched. The main parameters which have a greater impact on the speed of calculation results were discussed. And speed calculation methods based on uncertainty factors have been analyzed. By use of the vehicle vertical side collisions case, the speed of uncertainty has been carried out. Combined with accident surveillance video, the video picture computed speed and uncertainty factors obtained speed were compared. The results showed that selected road adhesion coefficient, vehicle weight and other parameters as the uncertainty factors, the use of uncertainty obtained speed with high reliability of forensic, which can be used in accident reconstruction

The Genetic Mechanism of Inertinite in the Middle Jurassic Inertinite-Rich Coal Seams of the Southern Ordos Basin

Inertinite is an important type of organic maceral in coal deposits, andalso an important geological information carrier of coal forming environments. In the southern section of the Ordos Basin, the No. 4 inertinite-richcoal seam of the Middle Jurassic Yan’an Formation in the Binchang Coalfield was selected as an example to study the genetic mechanism of theinertinite. In this study, the results obtained from experimental tests ofcoal rock, including principal and trace elements, stable carbon isotopes,scanning electron microscopy, inertinite reflectance, sporopollen andfree radical retorting methods, were analyzed. Then, the findings werecombined with the previous understanding of the oxygen content in theatmosphere and ground fire characteristics, in order to discuss the genesismechanism of inertinite in the No. 4 coal seam. The obtained researchresults were as follows: (1) During the coal forming period of the No. 4coal seam, the overall climate had been relatively dry. There were fourrelatively dry-wet climate cycles in the No.4 coal seam, which werecontrolled by the eccentricity astronomical period. The inertinite contentwere relatively high during the dry periods; (2) The temperature rangesuitable for microorganism activities during the oxidation processes wasbetween 0 and 80℃ . The simulation results of the free radical concentrations showed that the maximum temperature of fusain in the No. 4 coalseam during the process of coalification had not exceeded 300℃ , whichwas significantly higher than the temperature range of microorganismactivities. Therefore, these were not conducive to the activities of microorganism and formation of inertinite during the coal-forming period;(3) The genesis temperature of the inertinite in the No. 4 coal seam wascalculated according to the reflectance of the inertinite, which was lowerthan 400 ℃ . This result supported the cause of wildfire of the inertiniteand reflected that the type of wildfire was mainly ground fire, along withpartially surface fire. Moreover, the paleogeographic location, climaticconditions, atmospheric oxygen concentration, etc. of the study areashowed that the conditions for wildfire events were in fact available; (4)There were dense and scattered fusinite observed in the No. 4 coal seam,and the thickness of cell walls were found to differ. It was speculated thatthis was related to the type of wildfire, combustion temperatures, combustion timeframes, and different initial conditions of the burned objectsduring the coal forming periods