On the fatigue strength calculation of the welded shell structures from high-strength steels under low-cycle loading. Part 1: Estimation at the initial stage of fatigue failure

The normative methods for calculating the fatigue strength of welded joints are of limited use for low-cycle loads, as they do not take into account the possible variation in the asymmetry of the operating stress cycle, differences in the expected level of residual stresses, and the possible variety of joint geometry. Estimation procedures have been developed for shell structures made of high-strength steels subjected to external and internal pressure. They were based on experimental data on the resistance to fatigue fracture, physical modeling of individual stages of fatigue damage, and generalization of the results of numerical studies of the FEM of the stress-strain state.

dEchorate: a calibrated room impulse response dataset for echo-aware signal processing

This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.

A Self Supervised Convolutional Neural Network Outdoor Scene Relighting

Outdoor scene relighting is a difficult trouble that calls forexact know-how of the scene geometry, illumination and albedo. Currentstrategies are absolutely supervised, requiring excessive exceptional ar?tificial renderings to educate a answer. Such renderings are synthesizedthe usage of priors discovered from restrained facts. In contrast, we ad?vise a self-supervised technique for relighting. Our technique is educatedbest on corpora of pics accrued from the net with none user-supervision.This without a doubt infinite supply of education facts lets in educationa popular relighting answer. Our technique first decomposes an photointo its albedo, geometry and illumination. A novel relighting is thenproduced through enhancing the illumination parameters. Our answerseize shadow the usage of a committed shadow prediction map, and doesnow no longer depend on correct geometry estimation. We compare ourmethod subjectively and objectively the usage of a brand new datasetwith ground-reality relighting. Results display the capacity of our methodto provide photo-sensible and bodily achievable results, that generalizesto unseen scenes

The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation

This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formulate this problem, which is also known as simultaneous localization and mapping (SLAM), as a hybrid estimation problem. We derive the extended Kalman filter (EKF) for both tracking the robot’s own location and estimating the room geometry. Since the EKF employs linearization at every step, we incorporate a regulated kinematic model, which facilitates a successful tracking. In addition, we consider the echo-labeling problem as solved and beyond the scope of this paper. We then develop the hybrid Cramér-Rao lower bound on the estimation accuracy of both the localization and mapping parameters. The algorithm is evaluated with respect to the bound via simulations, which shows that the EKF approaches the hybrid Cramér-Rao bound (CRB) (HCRB) as the number of observation increases. This result implies that for the examples tested in simulation, the HCRB is an asymptotically tight bound and that the EKF is an optimal estimator. Whether this property is true in general remains an open question.