When estimating parameters it is important to determine both the most probable values and the confidence intervals. To do so accurately requires bayesianinference that can involve a high computational cost for comple...
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When estimating parameters it is important to determine both the most probable values and the confidence intervals. To do so accurately requires bayesianinference that can involve a high computational cost for complex models, particularly if the range of possible parameter values is large. One approach is to sample the model response surface and to interpolate. When done with a tensor grid the cost of developing an accurate representation of the surface and to locate the point of greatest probability can be prohibitively expensive. Sparse grids offer an alternative that may be of less cost. Because sparse grids are inherently approximations it is important to determine the accuracy of the representations since the errors may lead to less efficient calculations in estimating parameters and may distort the marginal probability distributions of the parameters. Except for atrociously ill-conditioned inverse problems sparse grids are particularly well suited for use with the least-squares approach and are even better for bayesianinference and the detection of the cause of ill-conditioning.
Modeling uncertainty or modeling error has been widely recognized as one major challenge in structural model updating for structural identification and damage detection. It renders model updating inherently ineffectiv...
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Modeling uncertainty or modeling error has been widely recognized as one major challenge in structural model updating for structural identification and damage detection. It renders model updating inherently ineffective in converging to the real structural model because of the physical bias present in establishing the numerical model. This study aims to minimize the influence of modeling uncertainty during model updating so that the updated model can accurately indicate the damage state. To this end, this study proposes a methodology that applies pattern recognition methods to guide bayesian model updating (BMU) and supervise the identification of structural damage. In detail, the transfer learning (TL) technique realized by domain adaptation is used to bridge the gap between the biased numerical model and the real structure and to guide the model updating process. Numerical and experimental studies have been implemented to validate the efficiency of domain adaptation in identifying the correct damage locations and the advantage of TL-guided BMU over the traditional method in identifying damage severities when modeling error exists. Moreover, this study proposes applying domain adaptation to bridge the gap between model-based and data-driven structural health monitoring (SHM) that are realized via model updating and pattern recognition, respectively. The proposed methodology is valuable and instructive for future work in this area.
This study proposes a real-time probabilistic estimation method for determining the thermal properties of backfill used in underground power transmission lines and ground heat exchangers. The thermal properties of bac...
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This study proposes a real-time probabilistic estimation method for determining the thermal properties of backfill used in underground power transmission lines and ground heat exchangers. The thermal properties of backfill, serving as a heat conductor between the heat source and surrounding soil, are crucial design parameters for underground transmission lines and ground heat exchangers because temperature limits their maximum operational output. However, an unpredictable environment and human fieldwork during on-site backfill construction lead to considerable performance uncertainty, requiring in situ thermal property estimation. Furthermore, when transmission lines are installed over long distances, on-site assessments are necessary at multiple locations, making it essential to reduce the experimental time. The proposed estimation method enables real-time monitoring of temporal changes in the estimated probability distribution of the thermal property and quantifies the estimation uncertainty. The proposed method also allows immediate decision-making regarding experiment termination by evaluating real-time convergence by calculating the Jensen-Shannon divergence of sequentially estimated probability distributions. The effectiveness of the proposed method was tested on two backfill materials, demonstrating its ability to determine the estimation convergence by capturing contextual information affecting the estimation uncertainty.
Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and wh...
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Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and whether these can serve as a model for clinical anxiety. Here we test whether anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders-particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchicalbayesian model of inference and learning, we assessed the effect of state anxiety on bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental uncertainty, and informational uncertainty about the reward tendency. Anxious individuals' beliefs about reward contingencies were more precise (had smaller uncertainty) and thus more resistant to updating, ultimately leading to impaired reward-based learning. State anxiety was also associated with greater uncertainty about volatility. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more willing to be uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, lower-level precision-weighted prediction errors (pwPEs) about reward tendencies were represented in the ERP signals across central and parietal electrodes peaking at 496 ms, overlapping with the late P300 in classical ERP analysis. The state anxiety group did not exhibit a significant representation of low-level
In this paper, we construct a bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale par...
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In this paper, we construct a bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that bayesian estimates have a good performance.
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