This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmen- tal, and other stresses for long-term prediction. Further- more, this work shows how these measures can be used to implement a novel feedback correction loop aimed to suggest modifications, at a system input level, with the purpose of extending the remaining useful life of a faulty nonlinear, non-Gaussian system. The correction scheme is tested and illustrated using real vibration feature data from a fatigue-driven fault in a critical aircraft compo- nent.

We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the industrial track of the com- petition in which a diagnosis algorithm must de- tect, isolate, and identify faults in an electrical power distribution testbed in order to provide cor- rect abort recommendations. Both diagnosis al- gorithms are based on a qualitative event-based fault isolation framework augmented with model- based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault iso- lation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.

APCC 계절예측은 11개국 16개 APEC 회원국 기상청 및 연구 기관으로부터 수집된 전지구 예측 모델의 300개 이상의 앙상블 자료를 종합한 것입니다.다중모델 단정예측(DMME: Deterministic MME), 다중모델 확률예측(PMME: Probabilistic MME) 결과를 제공하고 있습니다.단정예측 예측값은 편차로 제공되며 편차란 기후값 혹은 평년값과의 차이입니다.확률예측 확률값은 3분위의 범주로 나누어 제공되며 기온(강수)의 경우 평년보다 높을(많을) 확률, 평년과 비슷할 확률, 평년보다 낮을(적을) 확률로 표현됩니다.제공하고 있는 변수 강수, 기온, 지위고도, 해수면온도, 바람 등입니다.아시아태평양경제협력체 기후센터는 전지구 계절예측 검증 정보를 제공합니다.이 데이터는 검증 정보 생산을 위하여 필요한 관측자료인 재분석 자료의 변수 목록입니다.

The FSim wildfire simulation model is widely used to generate estimates of burn probability (BP). However, few studies have compared BP models to subsequent wildfires to assess their suitability for estimating near-future wildfire risk. Here, we compared the U.S.D.A. Forest Service’s publicly available BP map for the conterminous U.S. (Short et al., 2020; Dillon et al., 2023) to observed wildfire perimeters. Our main focus was to evaluate the BP map version based on 2014 LANDFIRE fuels data and calibrated to historical wildfires from 1992-2015, allowing us to compare BP to observed wildfires from 2016-2022. We also compared evaluations using a newer version of the BP map based on 2020 LANDFIRE fuels and 1992-2020 historical wildfires, and additionally performed evaluations for the western U.S. based on differing wildfire size classes. This dataset includes CSV tables summarizing burned area proportions by BP classes for individual pyromes, or regions with similar wildfire regimes, as well as processing code used to summarize burned area by BP classes.