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Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

Introduction. Road traffic injuries are the eighth leading cause of death globally and the leading cause of death among young people aged 15-29. Of individuals killed or injured in road traffic injuries, a large group comprises occupants sustaining a thorax injury in frontal crashes. The elderly are particularly at risk, as they are more fragile. The evaluation of the frontal crash performance of new vehicles is normally based on barrier crash tests. Such tests are only representative of a small portion of real-life crashes, but it is not feasible to test vehicles in all real-life conditions. However, the rapid development of computers opens up possibilities for simulating whole populations of real-life crashes using so-called stochastic simulations. This opportunity leads to the aim of this thesis, which is to develop and validate a simplified, parameterized, stochastic vehicle simulation model for the evaluation of passive restraint systems in real-life frontal crashes with regard to rib fracture injuries. Methods. The work was divided into five phases. In phase one, the geometry and properties of a finite element (FE) generic vehicle buck model were developed based on data from 14 vehicles. In the second phase, a human FE model was validated for oblique frontal crashes. This human FE model was then used to represent the vehicle occupant. In the third phase, vehicle buck boundary conditions were derived based on real-life crash data from the National Automotive Sampling System (NASS) and crash test data from the Insurance Institute for Highway Safety. In phase four, a validation reference was developed by creating risk curves for rib fracture in NASS real-life crashes. Next, these risk curves were compared to the risk of rib fractures computed using the generic vehicle buck model. In the final phase, injury mechanisms in nearside oblique frontal crashes were evaluated. Results. In addition to an averaged geometry, parametric distributions for 27 vehicle and boundary condition parameters were developed as guiding properties for the stochastic model. Particular aspects of the boundary conditions such as pulse shape, pulse angle and pulse severity were analyzed in detail. The human FE model validation showed that the kinematics and rib fracture pattern in frontal oblique crashes were acceptable for this study. The validation of the complete FE generic vehicle buck model showed that the model overestimates the risk of rib fractures. However, if the reported under-prediction of rib fractures (50-70%) in the NASS data is accounted for using statistical simulations, the generic vehicle buck model accurately predicts injury risk for senior (70-year-old) occupants. The chest injury mechanisms in nearside oblique frontal crashes were found to be a combination of (I) belt and airbag loading and (II) the chest impacting the side structure. The debut of the second mechanism was found for pulse angles of about 30 degrees. Conclusion. A parameterized FE generic passenger vehicle buck model has been created and validated on a population of real life crashes in terms of rib fracture risk. With the current validation status, this model provides the possibility of developing and evaluating new passive safety systems for fragile senior occupants. Further, an injury mechanism responsible for the increased number of outboard rib fractures seen in small overlap and near-side oblique frontal impacts has been proposed and analyzed. / Vinnova Project: Real Life Safety Innovations

User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.

>Magister Scientiae - MSc / Chemical speciation and the evaluation of species distribution is the key to understanding the potential of brines to form scale or corrode the water circuit as well as the potential of mobility and release trends of the pollutants into the environment. It is important to identify highly soluble free ions in water chemistry because toxicity of ions is related to mobility and consequently bioavailability. The chemical composition, character and chemical speciation modelling of saline effluents (brines) at Tutuka Power Station and Sasol Synthetic Fuels Complex in Secunda were studied. The form in which chemical species exist (chemical speciation) and the physical and chemical interactions of species in saline effluents at these two study sites is not fully understood. This study investigated how pH, temperature, alkalinity and chemical composition influenced chemical speciation, species distribution, scale forming and corrosion potentials of the different saline effluent streams at the two sites using computer programs PHREEQC and Aq.QA. Characterizations of the results were presented in Stiff and Piper diagrams generated by the Aq.QA computer software. Chemical speciation modelling of the brines showed that scale-forming minerals aragonite, calcite, hematite, anhydrite and gypsum have positive saturation indices between 0 and 20 in mine water, RO brine at Tutuka and Sasol Secunda, EDR brine at Sasol Secunda and VC brine at Tutuka Power Station. The water types at Tutuka Power Station were found to be mainly Na-S04 water types and those at Sasol Secunda were a mixture of Na-Cl and Na-S04 water types. Water treatment chemicals playa major role in increase were absent in the intake water. It was found that Sasol Secunda water streams are much more heavily contaminated than Tutuka water streams. The study also found that the mine water utilised at Sasol Secunda is two-fold more polluted than the mine water utilised at Tutuka although these sites are a mere 40 km apart. The sodium adsorption ratios showed that all the saline effluent streams at Tutuka and Sasol Secunda were unsuitable for irrigation, except for desalination product waters. Chemical speciation showed that the predominant species in the most concentrated saline effluent (VC brine) at Tutuka were the free cr ion at approximately 100 % with very minute quantities of FeCI+ and ZnCI+ and the predominant sodium species were the free Na+ ion which existed at 85 %. Magnesium species had the predominant form as the ionic compound MgS04 at 73 % and the carbonates were mainly in the form of NaC03- (53 %), HC03- (28 %) and CO{(7 %). The most concentrated brine analysed at Sasol Secunda was the TRO brine. PHREEQC did not predict the precipitation of CaC03 from the TRO brine at Sasol Secunda. The most abundant calcium species were Ca2+(59 %) ions and CaS04 (40 %). The brine was at a pH of 5.76 with dissolved CO2 at 73 % of the carbonate species. Trace elements were evaluated and the toxic trace elements varied from 0.07 mg/L (As) to 26.75 mg/L (Sr) at Sasol Secunda. At Tutuka Power Station the toxic trace elements in brines varied from 0.02 mg/L (As/Se) to 16.85 mg/L (Sr). Sr and B were found to be the most highly concentrated toxic elements. The major and trace ion chemistry, alkalinity, pH, sodium adsorption ratios, change in concentration of the water streams and the brine chemical composition after contact with ash was also evaluated. When saline effluents at Tutuka Power Station and Sasol Secunda are combined with ash, pH, Ca content and alkalinity of the resulting solution increased. The chemical composition of saline effluents can be influenced by the ingress of CO2 from the atmosphere. The study shows conclusively that brine composition and concentration is highly variable at these South African power utilities and processes such as RO, contact with ash and C02 ingress can have an impact upon the overall brine quality. Aq.QA was found to be a more accurate tool for classifying waters according to dominant ions than Stiff diagrams but Stiff diagrams still have the superior advantage of being a mapping tool to easily identify samples of similar composition as well as quickly identify what has been added or what has been removed from a water stream. Chemical speciation could identify effluent streams where C02 dissolution had taken place.

As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a safety architecture for resource constrained autonomous vehicles that enables the development of safer and more efficient nano-drone systems. The insight for the proposed safety architecture is that the regular structure of data-driven models used to control drones can be exploited to efficiently compress and identify key events that should be conserved in the EDR subsystem. We describe an implementation of the architecture, including hardware and software support, and quantify the benefits of the approach. We show that the proposed techniques can increase the amount of recorded flight time by over 10x and reduce energy usage by over 10x for high-resolution systems.

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