Learning to drive: from hazard detection to hazard handling

Hazard perception has been found to correlate with crash involvement, and has thus been suggested as the most likely source of any skill gap between novice and experienced drivers. The most commonly used method for measuring hazard perception is to evaluate the perception-reaction time to filmed traffic events. It can be argued that this method lacks ecological validity and may be of limited value in predicting the actions drivers’ will take to hazards encountered. The first two studies of this thesis compare novice and experienced drivers’ performance on a hazard detection test, requiring discrete button press responses, with their behaviour in a more dynamic driving environment, requiring hazard handling ability. Results indicate that the hazard handling test is more successful at identifying experience-related differences in response time to hazards. Hazard detection test scores were strongly related to performance on a driver theory test, implying that traditional hazard perception tests may be focusing more on declarative knowledge of driving than on the procedural knowledge required to successfully avoid hazards while driving. One in five Irish drivers crash within a year of passing their driving test. This suggests that the current driver training system does not fully prepare drivers for the dangers they will encounter. Thus, the third and fourth studies in this thesis focus on the development of two simulator-based training regimes. In the third study participants receive intensive training on the molar elements of driving i.e. speed and distance evaluation. The fourth study focuses on training higher order situation awareness skills, including perception, comprehension and projection. Results indicate significant improvement in aspects of speed, distance and situation awareness across training days. However, neither training programme leads to significant improvements in hazard handling performance, highlighting the difficulties of applying learning to situations not previously encountered.

Integrated genetic analysis systems

The overall objective of this thesis is to integrate a number of micro/nanotechnologies into integrated cartridge type systems to implement such biochemical protocols. Instrumentation and systems were developed to interface such cartridge systems: (i) implementing microfluidic handling, (ii) executing thermal control during biochemical protocols and (iii) detection of biomolecules associated with inherited or infectious disease. This system implements biochemical protocols for DNA extraction, amplification and detection. A digital microfluidic chip (ElectroWetting on Dielectric) manipulated droplets of sample and reagent implementing sample preparation protocols. The cartridge system also integrated a planar magnetic microcoil device to generate local magnetic field gradients, manipulating magnetic beads. For hybridisation detection a fluorescence microarray, screening for mutations associated with CFTR gene is printed on a waveguide surface and integrated within the cartridge. A second cartridge system was developed to implement amplification and detection screening for DNA associated with disease-causing pathogens e.g. Escherichia coli. This system incorporates (i) elastomeric pinch valves isolating liquids during biochemical protocols and (ii) a silver nanoparticle microarray for fluorescent signal enhancement, using localized surface plasmon resonance. The microfluidic structures facilitated the sample and reagent to be loaded and moved between chambers with external heaters implementing thermal steps for nucleic acid amplification and detection. In a technique allowing probe DNA to be immobilised within a microfluidic system using (3D) hydrogel structures a prepolymer solution containing probe DNA was formulated and introduced into the microfluidic channel. Photo-polymerisation was undertaken forming 3D hydrogel structures attached to the microfluidic channel surface. The prepolymer material, poly-ethyleneglycol (PEG), was used to form hydrogel structures containing probe DNA. This hydrogel formulation process was fast compared to conventional biomolecule immobilization techniques and was also biocompatible with the immobilised biomolecules, as verified by on-chip hybridisation assays. This process allowed control over hydrogel height growth at the micron scale.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.