The relationship between maritime education and employer trust: the structural equation modelling (SEM) perspective

The research is aimed to identify factors in the maritime education and training (MET) system and its relationship on maritime labour employers’ (MLEs) trust based on current practice in Malaysia. A questionnaire survey was used, and the respondents were the participants of a related conference in Malaysia. The structural equation modelling technique was used to explain the relationships in the stipulated research model. The test of structural model confirms that the model is adequate in explaining the MLEs’ trust. The results reveal that the perceived MET institutions’ efficiency has significant direct effects on the MLEs’ trust and the perceived seafarers’ competency. In addition, the perceived implementation of International Convention was found to significantly affect the seafarers’ competency. However, the perceived seafarers’ competency was not found to directly affect the MLEs’ trust. This study suggests that the MLEs’ trust is an important indicator and feedback on it can be used to further enhance the current MET system and its policies. Thus, one of the strategies is to develop a policy in order to achieve an effective maritime education and training. This is to support the maritime communities in progressing towards a more sustainable development in the MET in the context of enhancing the maritime safety.     

Links between subjective assessments and objective metrics for steering

The characteristics of steering perception are decisive factors for overall driver preference and for vehicle safety. Car manufacturers are continuously required to tune the characteristics of the vehicle and have a strong need to be more effective in the design and evaluation of cars. Using only objective metrics (OM) can result in unwanted steering feel and using only subjective assessments (SA) is time-consuming, costly and non-repetitive. Before a tool can be built to predict the steering feel in front-end development and to improve design knowledge from the full vehicle level to the component level, links between subjective assessments and objective metrics must be found and analysed. The data collected for the study presented in this paper include subjective ratings from expert drivers and objective measurements made with steering robots, involving twelve expert drivers and over twenty vehicles across four different vehicle classes. Linear regression and neural network analysis (NN) have been used to explore reliable subjective-objective links. The tools and methods used in this research showed promising results. Most of the links found concern response and torque feedback. The preferred ranges of some crucial objective metrics leading to more desirable steering feel have been defined and presented. The results indicate that it would be possible for car manufacturers to develop new vehicles more effectively with a steering feel in line with the design criteria by using the tools and methods investigated in this paper.     

Analysis and optimisation of objective vehicle dynamics testing in winter conditions

Objective testing of vehicle handling in winter conditions has not been implemented yet because of its low repeatability and its low signal-to-noise ratio. Enabling this testing, by identifying robust manoeuvres and metrics, was the aim of this study. This has been achieved by using both experimental data, gathered with steering-robot tests on ice, and simulation models of different complexities. Simple bicycle models with brush and MF-tyre models were built, both optimally parameterised against the experimental data. The brush model presented a better balance in complexity performance. This model was also implemented in a Kalman filter to reduce measurement noise; however, a simpler low-pass filter showed almost similar results at lower cost. A more advanced full vehicle model was built in VI-CarRealTime, based on kinematics and compliance data, damper measurements, and real tyre measurements in winter conditions. This model offered better results and was therefore chosen to optimise the initial manoeuvres through test design and simulations. A sensitivity analysis (ANOVA) of the experimental data allowed one to classify the robustness of the metrics. Finally, to validate the results, the proposed and the initial manoeuvres were tested back to back in a new winter campaign.