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1. Simultaneous Bayesian Calibration and Engineering Design With an Application to a Vibration Isolation System

   https://doi.org/10.1115/1.4050075  

   Ehrett, Carl; Brown, D. Andrew; Kitchens, Christopher; Xu, Xinyue; Platz, Roland; Atamturktur, Sez (March 2021, Journal of Verification, Validation and Uncertainty Quantification)

   null (Ed.)

   Abstract Calibration of computer models and the use of those design models are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach to the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized. 

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2. Comparative analysis of circular and linear fringe projection profilometry: from calibration to 3D reconstruction

   https://doi.org/10.1364/OPTCON.520433  

   Li, Jiaqiong; Li, Beiwen (March 2024, Optics Continuum)

   This study compares the accuracy of circular and linear fringe projection profilometry in the aspects of system calibration and 3D reconstruction. We introduce, what we believe to be, a novel calibration method and 3D reconstruction technique using circular and radial fringe patterns. Our approach is compared with the traditional linear phase-shifting method through several 2 × 2 experimental setups. Results indicate that our 3D reconstruction method surpasses the linear phase-shifting approach in performance, although calibration efficiency does not present a superior performance. Further analysis reveals that sensitivity and estimated phase error contribute to the relative underperformance in calibration. This paper offers insights into the potentials and limitations of circular fringe projection profilometry. 

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3. Influencing Confidence: Testing Ways to Increase or Decrease Confidence in Knowledge

   https://doi.org/10.1002/bdm.70048  

   Stone, Eric R; Parker, Andrew M; Somerville, Annie H; Nixon, Brooke; Kemmerly, Rowan; Bongard, Michelle M (December 2025, Journal of Behavioral Decision Making)

   ABSTRACT This paper examines approaches for influencing people's confidence in their knowledge without influencing knowledge. Three studies examined the relative effectiveness of training and false feedback approaches. Participants chose which of two IKEA products they thought was more expensive and indicated their confidence in that judgment for 50 product pairs. In Study 1, participants took part in one of five conditions designed to manipulate their confidence: false feedback‐increasing, false feedback‐decreasing, training‐increasing, training‐decreasing, or control. For false feedback, we told participants they did very well or poorly on the task. For training‐increasing, we gave participants information about IKEA pricing that appeared useful but was difficult to implement. For training‐decreasing, we developed an automated calibration training technique that provided personalized calibration feedback consisting of a calibration diagram accompanied by textual summary information and advice. Neither the false feedback nor training approach increased confidence on 50 subsequent knowledge‐confidence judgments. However, both manipulations designed to reduce confidence were successful, with a substantially larger effect in the calibration training condition. In Study 2, we adapted the calibration training approach to provide false feedback indicating participants were either underconfident or overconfident. Both the original calibration training pproach and the new false feedback approach indicating overconfidence reduced confidence, and the false feedback approach indicating underconfidence increased confidence. Study 3 tested the effectiveness of this new false feedback approach on an on‐line rather than student sample, finding essentially the same results as those in Study 2. Throughout the three studies, the effects of the manipulations extended to overconfidence, overall calibration, and the Brier score. The results provide a potential tool for research and practice regarding confidence in knowledge. 

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4. Cone Penetration Testing to Constrain the Calibration Process of a Sand Plasticity Model for Nonlinear Deformation Analysis

   https://doi.org/10.1201/9781003308829-43  

   A. Chiaradonna; T.J. Carey; K. Ziotopoulou; J.T. DeJong (April 2022, Cone Penetration Testing 2022)

   A reliable prediction of liquefaction-induced damage typically requires nonlinear deformation analyses with an advanced constitutive soil model calibrated to the site conditions. The calibration of constitutive models can be performed by relying primarily on a combination of commonly available properties and empirical or semi-empirical relationships, on laboratory tests on site-specific soils, on in-situ penetration tests, or a combination thereof. Chiaradonna et al. (2022) described a laboratory-based calibration approach of the PM4Sand constitutive model and evaluated the prediction accuracy against the response of a centrifuge experiment of a submerged slope. This paper addresses an alternate calibration approach in which the PM4Sand model is calibrated using centrifuge in-situ CPT data. The model performance for the resulting calibration is evaluated against the centrifuge experimental data and prior simulations from Chiaradonna et al. (2022). In this case, the CPT-based calibration resulted in more accurate estimations of the dynamic response and permanent displacements. 

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5. Self-Supervised Camera Self-Calibration from Video

   https://doi.org/10.1109/ICRA46639.2022.9811784  

   Fang, Jiading; Vasiljevic, Igor; Guizilini, Vitor; Ambrus, Rares; Shakhnarovich, Greg; Gaidon, Adrien; Walter, Matthew R (May 2022, IEEE)

   Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. 

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