Influence of the geometric parameters of the vehicle frontal profile on the pedestrian’s head accelerations in case of accidents

The goal of this paper is to determine how the geometry of the vehicle’s frontal profile is influencing the pedestrian’s head accelerations (linear and angular) in car-to-pedestrian accidents. In order to achieve this goal, a virtual multibody dummy of the pedestrian was developed and multiple simulations of accidents were performed using vehicles with different frontal profile geometry, from different classes. The type of accidents considered is characteristic for urban areas and occur at relatively low speed (around 30 km/h) when an adult pedestrian is struck from the rear and the head acceleration variation are the measurement of the accident severity. In the accident simulation 3D meshes were applied on the geometry of the vehicles, in order to define the contact surface with the virtual dummy, similar with real vehicles. The validation of the virtual pedestrian dummy was made by performing two crash-tests with a real dummy, using the same conditions as in the simulations. The measured accelerations in the tests were the linear and angular accelerations of the head during the impact, and these were compared with the ones from the simulations. After validating the virtual model of the car-to-pedestrian accident, we were able to perform multiple simulations with different vehicle shapes. These simulations are revealing how the geometric parameters of the vehicle’s frontal profile are influencing the head acceleration. This paper highlights the main geometric parameters of the frontal profile design that influence the head injury severity and the way that the vehicles can be improved by modifying these parameters. The paper presents an approach to determine the “friendliness” of the vehicle’s frontal profile in the car-to-pedestrian collision.     

Belief and fuzzy theories for driving behavior assessment in case of accident scenarios

The estimation of the overspeed risk before the accident is among the main goals of this paper. The proposed method uses the Energy Equivalent Speed (EES) to assess the severity of an eventual accident. However, the driver behavior evaluation should take into account the parameters related to the Driver, the Vehicle and the Environment (DVE) system. For this purpose, this paper considers a two-level strategy to predict the global risk of an event using the Dempster-Shafer Theory (DST) and the Fuzzy Theory (FT). This paper presents two methods to develop the Expert Model-based Basic Probability Assignment (EM based BPA), which is the most important task in the DST. The first one is based on the accident statistics and the second method deals with the relationship between the Fuzzy and Belief measurements. The experimental data is collected by one driver using our test vehicle and a Micro-intelligent Black Box (Micro-iBB) to collect the driving data. The sensitivity of the developed models is analysed. Our main evaluation concerns the Usage Based Insurance (UBI) applications based on the driving behavior. So, the obtained masses over the defined referential subsets in the DST are used as a score to compute the driver’s insurance premium.     

Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures. The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models. In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction. Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway. While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 78.0 %, which is a relatively high accuracy in the human factors domain. The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs.     

Product Quality Evaluation Method Based on Product Gene Theory

Traditional quality inspection based product quality evaluation method with complex process has high operating cost and requires more professional knowledge. To remove the above limitation, this paper leads product gene theory into product quality evaluation. Methods of quality influencing factors based modeling and encoding are established. Combined with similarity theory and product gene theory, a product gene similarity analysis based quality evaluation method is proposed. The proposed method is low cost, operates easily and requires less specialized knowledge. A case study is conducted to prove the correctness and feasibility of this method.     

Reliability Modeling and Maintenance Policy Optimization for Deteriorating System Under Random Shock

Performance degradation and random shock are commonly regarded as two dependent competing risks for system failures. One method based on effective service age is proposed to jointly model the cumulative effect of random shock and system degradation, and the reliability model of degradation system under Nonhomogeneous Poisson processes (NHPP) shocks is derived. Under the assumption that preventive maintenance (PM) is imperfective and the corrective maintenance (CM) is minimal repair, one maintenance policy which combines PM and CM is presented. Moreover, the two decision variables, PM interval and the number of PMs before replacement, are determined by a multi-objective maintenance optimization method which simultaneously maximizes the system availability and minimizes the system long-run expect cost rate. Finally, the performance of the proposed maintenance optimization policy is demonstrated via a numerical example.     

H_∞ Autopilot Design for Autonomous Underwater Vehicles

The design approach of H∞ autopilot for autonomous underwater vehicles (AUVs) is proposed. Comprised by the three sub-controllers,i.e. speed,heading and depth controllers,the designed autopilot has advantage over existing H∞ control of AUVs. The overshoot in speed,heading and depth control systems under step commands is restricted by refining the weighting function for robust stability. The dynamic performance of heading and depth control systems is improved by feeding back yaw rate and pitch angle,respecti...     

Mechanical behaviors of the foot after individual releases of plantar fascia and ligaments during the balanced standing

To quantify mechanical effects of plantar fascia and ligaments on the arch structure, a 3D finite element model of the foot was created to simulate the balanced standing posture. Four cases after individual releases of plantar fascia, spring ligament, long and short plantar ligaments were simulated respectively to compare their consequences to the predictions of the intact structure. It was founded that the foot arch didn’t collapse obviously after any individual release of these structures. As plantar fascia was released, tensions of plantar ligaments were largely increased. Long and short plantar ligaments performed mutual compensation functions.     

Study on dynamic characteristics of vehicle gearbox under multi-boundary conditions

With the development of vehicle gearbox to high-power-density and high-speed, how to predict and optimize the dynamic characteristics of vehicle gearbox becomes increasingly prominent. Aiming at the vehicle gearbox, this paper comprehensively and deeply studies the dynamic characteristics under the multi-boundary conditions. The generation mechanism of the multi-source excitations triggering the gearbox vibration is analyzed firstly. The vibration transfer path of the gearbox is explored. Secondly, the engine excitation, the gear meshing excitation and the bearing support load are numerically calculated. According to the finite element method, a fluid-solid coupling finite element model of the gearbox body is established to predict the gearbox dynamic responses based on the Galerkin method and the Hamiltonian variational principle. Finally, the effects of the excitation condition, oil height and reinforcement forms on the vibration responses of the gearbox body are thoroughly studied by simulation. The analysis indicates that it not only helps to modify and improve the method of forecasting the gearbox dynamic response, and also provides the theoretical and technical guidance for the gearbox design and optimization.     

Automatic outer surface extraction of femoral head in CT images

Computer-aided hip surgery planning and implant design applications require accurate segmentation of femoral head and proximal acetabulum. An accurate outer surface extraction of femoral head using marching cubes algorithm remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we present an automatic and fast approach for segmentation of femoral head and proximal acetabulum which leads to accurate and compact representation of femoral head using marching cubes algorithm. At first, valley-emphasized images are constructed from original images so that valleys stand out in high relief. Otsu’s multiple thresholding technique is applied to seperate the images into bone and non-bone classes. Region growing method and threedimensional (3D) morphological operations are performed to fill holes in the bone. In the reclassification process, the bone regions are further segmented, and the boundaries of the bone regions are further refined based on Bayes decision rule. Finally, marching cubes algorithm is applied to reconstruct a 3D model and extract the outer surface of femoral head and proximal acetabulum. Experimental results show that this method is an accurate segmentation technique for femoral head and proximal acetabulum and it can be applied as a tool in medical practice.     

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance.