Technical complications in expelling and converting Domba oil into biodiesel

Desiccated resinous ‘Domba’ (Calophyllum inophyllum L.) kernels were mechanically expelled to obtain oil. The highly viscous cold pressed oil was then converted into biodiesel using four different transesterification protocols in a newly developed programmable biodiesel reactor. This paper describes the difficulties associated with expelling and converting Domba oil from the kernels, and the efforts made to overcome the problems. Amongst the four methods tested, 4-stage transesterification protocol was found to yield better quality biodiesel than the other protocols

Delivery of online electronics and mechatronics labs during lockdowns

This paper provides a detailed explanation of several approaches that can be used to conduct online labs for electronics/mechatronics engineering courses and explains the results obtained from a survey conducted. The detailed explanations provide information on how to implement the method, benefits of the stated process, possible challenges, and how to overcome those challenges. Furthermore, this paper presents the analyzed results from a survey conducted to capture the student experience in online labs

Industry-led mechatronics degree development in regional Australia

This paper presents a technique that was used in the recent development of a new Mechatronics degree in Australia. This technique addressed the local industry needs and the available resources for a well-balanced Mechatronics degree program. The degree development was based on project-based learning and industry engagement. The development of the new Mechatronics degree was made possible via a State Government grant of AU$2.4 Million which was matched by industry contribution of AU$10 Million in cash and in-kind. Since industry was a major stake holder in this degree, a specific industry survey was conducted to check the desired graduates attributes, from industry point of view. The results of this survey is also included in this papers. In addition, the program also addressed the regional industry's challenge of retaining qualified engineers via a clear pathway program for students knowledge and skills development. This paper presents industry's anticipated outputs of the academic Mechatronics program. In addition the paper also discusses the mechanisms adopted for the development of this new degree. The developed fully integrated Mechatronics program was founded on the realisation that if a person undertook a mechanical degree followed by an electronics degree followed by a computer science degree, that person is, still, NOT a Mechatronics engineer

New artificial intelligence based tire size identification for fast and safe inflating cycle

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle’s user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle’s user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires.This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of in put parameters. The test results are analyzed and discussed in this paper

Novel tire inflating system using extreme learning machine algorithm for efficient tire identification

Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process

Views of American and Australian mobility device users and ambulant bus users regarding occupant restraint systems on public buses

Introduction: With an ageing population, increasing numbers of people are using mobility devices, such as wheelchairs or scooters, whilst travelling on public route buses. The regulations and availability of active (wheelchair tie down and occupant restraint systems or WTORS) and passive (rearward facing) mobility device restraint systems on buses varies between countries. To date few studies have investigated passenger feedback on the use of restraint systems. This study aimed to gather feedback about WTORS on buses from passengers where these are in use (United States) and not in routine use (Australia) to guide decisions on their introduction. Methods: A prospective study using a purpose-designed electronic survey. Participants, predominantly recruited by Qualtrics, comprised two groups; mobility device and ambulant bus users in two countries; Australia and the United States (US). Results: The 448 participants rated the top two most important factors when deciding if buses should have WTORS as safety and comfort. Ninety-two percent of respondents believed people using mobility devices should use a WTORS which was rated 7.66/10 (SD1.97) as effective to prevent injuries to self or others. Only a minority of participants (13.2%) had ever slid or fallen from their mobility device, or seen a person slide or fall (13.6%) while on a bus with no differences between countries despite WTORS not being in use in Australia. Respondents reported it was OK to delay a journey an average of 5.52 (SD 2.89) minutes to secure/release a restraint system, which compares favourably to literature-reported real time of one to 4 min. Conclusions: Although WTORS were widely perceived by participants as important for safety, questions concerning their effectiveness to prevent slide or tip remain. Prior to the introduction of any securement system in Australia, the effectiveness of passive occupant containment systems to prevent slide or tip also warrants investigation