Within the Cluster of Excellence "Tailor-Made Fuels from Biomass", a new reaction sequence to transform biomass into 2-methylfuran has been developed. In the present study, the influence of this potential biofuel on in-cylinder spray formation and evaporation as well as engine performance is studied experimentally using a direct-injection spark-ignition single-cylinder research engine. The results obtained for 2-methylfuran are benchmarked against investigation on the same engine using conventional research octane number (RON) 95 fuel and ethanol. The in-cylinder spray formation and evaporation process is characterized by high-speed Mie scattering visualizations, indicating quicker evaporation of 2-methylfuran compared to ethanol. Engine experiments support the findings of the optical measurements by revealing excellent combustion stability, especially in cold conditions, combined with a hydrocarbon emission reduction of at least 61 % in the relevant spark timing range compared to conventional fuel. The enleanment capability was also found to be higher by 0.16 units of relative air/fuel ratio. A noticeable drawback resulting from the combustion of 2-methylfuran is higher emissions of nitrogen oxides. The knock resistance of 2-methylfuran at full load is significantly better compared to RON 95, however, worse than ethanol. It allows for a compression ratio increase of more than 3.5 units compared to RON 95. The measured efficiency benefits with a compression ratio increase of 3.5 units range up to 9.9 % at full load.
Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the device. Our recognition system is designed for everyday use: it allows flexible placement of the device (no assumed or fixed position), requires only minimal personalization effort from the user (1-3 minutes per activity) and is capable of detecting a high number of activities. The components of the service are shown in an evaluation scenario, in which recognition rates up to 97% can be achieved for ten activity classes. User Accuracy (%) User Accuracy (%) Combination V tr V ck Combination V tr V ck
Abstract-A variety of studies in the past decades have shown that fine particulate matter can be a serious health hazard, contributing to respiratory and cardiovascular disease. Due to this, more and more regulations defining certain permissible concentration limits have been set by governments around the world. However, current standard measurement equipment is large, expensive and sparsely deployed. Additionally, both the exposure to hazardous conditions and the susceptibility to negative health effects vary from person to person. As a result, we see the need for fine-grained, mobile and distributed measurements, e.g. to identify hot spots or monitor people at risk. Our research investigates the feasibility of particulate matter measurements using cheap, commodity dust sensors which are small enough to be incorporated into mobile devices. This paper first discusses application scenarios which would benefit from inexpensive methods to assess the particulate matter load. Subsequently, commercial-off-the-shelf (COTS) sensors are compared and their general suitability for the application scenarios is examined. Finally, an experimental setup for the evaluation of one of the sensors is presented along with preliminary results.
Abstract. This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial o-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we tted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-y calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-y cross-device data processing have been done.
Citizen Science with mobile and wearable technology holds the possibility of unprecedented observation systems. Experts and policy makers are torn between enthusiasm and scepticism regarding the value of the resulting data, as their decision making traditionally relies on high-quality instrumentation and trained personnel measuring in a standardized way. In this paper, we (1) present an empirical behavior taxonomy of errors exhibited in non-expert smartphone-based sensing, based on four small exploratory studies, and discuss measures to mitigate their effects. We then present a large summative study (N=535) that compares instructions and technical measures to address these errors, both from the perspective of improvements to error frequency and perceived usability. Our results show that (2) technical measures without explanation notably reduce the perceived usability and (3) technical measures and instructions nicely complement each other: Their combination achieves a significant reduction in observed error rates while not affecting the user experience negatively.
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