Experimental Study of Nuclear Gyro Bias Compensation Using the Quality Factor Estimation

The possibility for bias compensation of nuclear gyro using the quality factor estimation is shown. The corresponding method is described. A description of its application for a nuclear gyroscope in the angle sensor mode is given. The results of experiments confirming the effectiveness of the presented method of nuclear gyro signal refinement are presented.

Finite Element Model-Based Weight-Over Process Philosophy for Bridge Loading Capacity Evaluation and Rating Factor Estimation

Existing rating methods estimate bridge loading capacity and demand from secondary actions due to live loads in the primary structural components. In these methods, uniaxial yielding stress is traditionally used to detect component capacity using either stress quantities or shear-moment actions to compute the capacity demand of the bridge. These approximations can lead to uncertainties in load capacity estimation. This article presents the weight-over process (WOP), a novel computer-aided approach to bridge loading capacity evaluation based on tonnage and rating factor estimation. WOP is expected to capture different forms of failure in a more general manner than existing methods. In WOP, a bridge finite element model (FEM) is discretized into many sections and element sets, each containing a single material type, and each assigned a suitable 3D failure criterion. Then, factored gross vehicle weights (GVWs) are incrementally imposed on the bridge FEM with those predefined ultimate unfavored loading scenarios in a manner similar to proof load testing. WOP code runs nonlinear analysis at each increment until a stopping criterion is met. Two representative bridges were selected to confirm WOP’s feasibility and efficacy. The results showed that WOP-predicted values at the interior girders were between those of the conventional AASHTO and the nondestructive testing (NDT) strain measurement methods. That may put WOP in a favorable zone as a new method that is less conservative than AASHTO but more conservative than real NDT testing.

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

Dynamic factor models: Does the specification matter?

Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

A review on the prediction and assessment of powder factor in blast fragmentation

Powder factor can be defined as the quantity of explosives (kg) required to break a unit volume or tonne (t) of rock. The prospect of excavating rocks by blasting is characterized by a specific consumption of explosives. In the past decades, researchers have come up with several precise approaches to predict powder factor or specific charge in blast operations other than through trial blast. Research in this area has focused on the relationship between rock mass properties, blasting material and blasting geometry to establish the powder factor. Also, the interaction between specific energy and particle size embodied in the theory of comminution that is less dependent on local conditions has been studied. In this paper, the various methods for powder factor estimation based on empirical and comminution theory modelling as well as machine learning approaches in both surface bench blasting and underground tunnel operations have been reviewed. The influence of intact rock properties on powder factor selection and the influence of powder factor selection on post-blast conditions have also been discussed. Finally, the common challenges that have been encountered in powder factor estimations have been pointed out in this regard.