A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks.

There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and tracking method is proposed based on the fusion of Lidar and millimeter-wave radar. It advances a secondary segmentation algorithm suitable for open-pit mine production scenarios to improve the detection distance and accuracy of small irregular obstacles on unpaved roads.

A feature-supervised generative adversarial network for environmental monitoring during hazy days.

The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days.

Development and validation of a semi-automatic landmark extraction method for mesh morphing.

Currently, landmark-based mesh morphing technology is widely used to rapidly obtain meshes with specific geometry, which is suitable to develop parametric human finite element (FE) models. However it takes too much time for landmark extraction to obtain high geometric accuracy. The purpose of this study is to develop and validate a semi-automatic landmark extraction method to reduce the time of manual selection of landmarks without sacrificing the accuracy of identifying landmarks in the process of mesh morphing.

Ultrasonic Inspection of Localized Defects in Low-Porosity CFRP.

A preliminary backscattered signal model of carbon-fiber-reinforced plastic (CFRP) laminate was established. The backscattered signal model was composed of three sub models, which were concerned with structural signal, scattering signal, and non-acoustic noise. Resonance in structural signal and echoes excited by defects (porosity and rich-resin) were studied.

Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition.

The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD).

The Influence of Neck Muscle Activation on Head and Neck Injuries of Occupants in Frontal Impacts.

The aim of the present paper was to study the influence of neck muscle activation on head and neck injuries of vehicle occupants in frontal impacts. A mixed dummy-human finite element model was developed to simulate a frontal impact. The head-neck part of a Hybrid III dummy model was replaced by a well-validated head-neck FE model with passive and active muscle characteristics.

Parametric analysis of occupant ankle and tibia injuries in frontal impact.

Objective: Non-fatal tibia and ankle injuries without proper protection from the restraint system has gotten wide attention from researchers. This study aimed to investigate occupant tibia and ankle injuries under realistic frontal impact environment that is rarely considered in previous experimental and simulant studies.

Methods: An integrated occupant-vehicle model was established by coupling an isolated car cab model and a hybrid occupant model with a biofidelic pelvis-lower limb model, while its loading conditions were extracted from the realistic full-frontal impact test.