Survey and Method for Determination of Trajectory Predictor Requirements

A survey of air-traffic-management researchers, representing a broad range of automation applications, was conducted to document trajectory-predictor requirements for future decision-support systems. Results indicated that the researchers were unable to articulate a basic set of trajectory-prediction requirements for their automation concepts. Survey responses showed the need to establish a process to help developers determine the trajectory-predictor-performance requirements for their concepts. Two methods for determining trajectory-predictor requirements are introduced. A fast-time simulation method is discussed that captures the sensitivity of a concept to the performance of its trajectory-prediction capability. A characterization method is proposed to provide quicker, yet less precise results, based on analysis and simulation to characterize the trajectory-prediction errors associated with key modeling options for a specific concept. Concept developers can then identify the relative sizes of errors associated with key modeling options, and qualitatively determine which options lead to significant errors. The characterization method is demonstrated for a case study involving future airport surface traffic management automation. Of the top four sources of error, results indicated that the error associated with accelerations to and from turn speeds was unacceptable, the error associated with the turn path model was acceptable, and the error associated with taxi-speed estimation was of concern and needed a higher fidelity concept simulation to obtain a more precise resul

A priori error estimation of magnetic material characteristics using stochastic uncertainty analysis

By interpreting electromagnetic or mechanical measurements with a numerical model of the considered electromagnetic device, magnetic properties of the magnetic circuit of that device can be estimated by solving an inverse numerical electromagnetic problem. Due to measurement noise and uncertainties in the forward model, errors are made in the reconstruction of the material properties. This paper describes the formulation and implementation of the error estimation and the prediction of which measurements that need to be carried out for accurate magnetic material characterization. Stochastic uncertainty analysis, based on Cramér-Rao bound (CRB), is introduced and applied to the magnetic material haracterization of a Switched Reluctance Motor (SRM) starting from mechanical (torque) and local magnetic measurements. The traditional CRB method that estimates the error due to measurement noise is extended with the incorporation of stochastic uncertain geometrical model parameters

Real-time absorption and scattering characterization of slab-shaped turbid samples obtained by a combination of angular and spatially resolved measurements

We present a fast and accurate method for real-time determination of the absorption coefficient, the scattering coefficient, and the anisotropy factor of thin turbid samples by using simple continuous-wave noncoherent light sources. The three optical properties are extracted from recordings of angularly resolved transmittance in addition to spatially resolved diffuse reflectance and transmittance. The applied multivariate calibration and prediction techniques are based on multiple polynomial regression in combination with a Newton-Raphson algorithm. The numerical test results based on Monte Carlo simulations showed mean prediction errors of approximately 0.5% for all three optical properties within ranges typical for biological media. Preliminary experimental results are also presented yielding errors of approximately 5%. Thus the presented methods show a substantial potential for simultaneous absorption and scattering characterization of turbid media

FRF Estimation through Sweep Milling Force Excitation (SMFE)

AbstractInaccurate machine dynamics characterization is thought to be one of the main sources of errors in current cutting stability models. For this reason, traditional dynamic characterization procedures have been called into question. A new method for frequency response function (FRF) estimation using the real milling force as the input excitation is proposed. It consists of exciting the structure through several cutting tests at increasing or decreasing spindle speed while measuring its response. This sweep milling force excitation (SMFE) procedure allows obtaining the FRF under real cutting conditions. The results obtained have improved stability prediction with respect to conventional impact tests

The colonoscopist's guide to the vocabulary of colorectal neoplasia: histology, morphology, and management

Prevention of colorectal cancer by colonoscopy requires effective and safe insertion technique, high level detection of precancerous lesions, and skillful use of curative endoscopic resection techniques. Lesion detection, characterization, use of appropriate resection methods, prediction of cancer at colonoscopy, and management of malignant polyps, all depend on an accurate and complete understanding of an extensive vocabulary describing the histology and morphology of neoplastic colorectal lesions. Incomplete understanding of vocabulary terms can lead to management errors. We provide a colonoscopist’s perspective on the vocabulary of colorectal neoplasia, and discuss the interaction of specific terms with management decisions

Uncertainty Quantification over Graph with Conformalized Graph Neural Networks

Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates. Given an entity in the graph, CF-GNN produces a prediction set/interval that provably contains the true label with pre-defined coverage probability (e.g. 90%). We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage. Moreover, besides valid coverage, it is crucial to reduce the prediction set size/interval length for practical use. We observe a key connection between non-conformity scores and network structures, which motivates us to develop a topology-aware output correction model that learns to update the prediction and produces more efficient prediction sets/intervals. Extensive experiments show that CF-GNN achieves any pre-defined target marginal coverage while significantly reducing the prediction set/interval size by up to 74% over the baselines. It also empirically achieves satisfactory conditional coverage over various raw and network features.Comment: Published at NeurIPS 202