Abstract

Media use plays an important role in the social, emotional, and cognitive development of young individuals and accounts for a large portion of their time. For this reason it is important to understand the variables that contribute to improve the use of the Internet as a source of information and knowledge in formal and informal contexts. How is it possible to exploit the huge potential of this tool to help people learn? What are the cognitive and social characteristics that help individuals experience the Internet without being overwhelmed by its negative effects? What skills are needed to select and manage information and communication? What type of Internet use creates new relationships and ways of learning? A sample of 191 subjects was examined to determine certain characteristic differences between subjects with high and low levels of Internet use. The results show that individuals with high levels of Internet use have higher extroversion and openness scores. The research analyses the use of the Internet in informal contexts to determine the benefits that may result from Internet use in education which may include the development of the skill set necessary to evaluate information critically and analytically and build independent attitudes.

Abstract

More and more Advanced Driver Assistance Systems (ADASs) are entering the market for improving both safety and comfort. Adaptive Cruise Control (ACC) is an ADAS application that has high interaction with the driver. ACC systems use limited sensor input and have only few configuration possibilities. This may result in the behaviour of the ACC not matching user's preferences in all cases, resulting in lower acceptance of the system. In this work, we examine the possibilities for a Personalised ACC (PACC), which adapts the ACC settings such that it matches the driver preference in order to increase the acceptance. The driver preferred ACC behaviour is predicted using machine learning techniques and manual driving data. On-road experiments showed that the method is promising as it is able to discriminate between two preference clusters with an accuracy of 85%.

Abstract

ggregated demand response for smart grid services is a growing field of interest especially for market participation. A growing trend in research is to utilize aggregated demand response for multiple smart grid services. This could be multi-market trading (day ahead, intraday and imbalance) or a combination of market and ancillary services such offering reserve power or congestion management. However, there is a potential conflict of interest when offering the same resource for simultaneous services. This work investigates the impact, both from a monetary and network stability perspective, of applying a predictive control trading strategy which actively offers aggregated flexibility of electric vehicles on both the German EPEX day ahead and intraday markets. An artificial neural network was used to forecast the available ramp up and down capacity of a Virtual Power Plant (VPP) of 1000 electric vehicles. Using this information, the available flexibility is traded to ramp up in one quarter and down in the next depending on the price delta seen in the intraday market. A number of simulation runs are done, each with different levels of flexibility traded. In every run, one week of realistic VPP behaviour is simulated. The total earnings on the intraday market are calculated as well as imbalance cost and imbalance power generated over this period. It was seen that with an increased offer of available flexibility, there was an increase in both total revenues up to ∼4200 euros for one week of trade as well as imbalance generated, ∼1.6 MWh. Therefore, there is a clear need for effective regulation that limits imbalance without losing the future grid-stabilising effects of the flexibility aggregator.

Abstract

Current advanced driver assistance systems (ADAS) measure and evaluate every environment scenario in each trip from scratch. They are not able to draw on information of previous trips because currently a memory function in production vehicles does not exist. Learning systems for vehicles were proposed to aggregate information gathered by driving routes multiple times. This information can be used to create a learning map and enable highly automated driving. Learned information can represent a certain state of the environment captured by sensors like speed limits or geo-positions of roadworks. Changes in the vehicle relevant environment will affect vehicle perception and may affect the distribution of measured variables over time. The instant where the underlying distribution of a variable changes is denoted as change point. This can invalidate parts of the acquired knowledge and was not sufficiently dealt with in previous publications in the automotive field. The ability to detect the mentioned change points is essential for the necessary ongoing update of the learning map by devaluing or removing any invalidated knowledge from memory. For this purpose we propose a statistical hypothesis test and investigate its application for a prototypic ADAS: a Curve Warning Assistance System. The formulated test is able to detect change points faster and more robust than previously proposed algorithms. The ability to aggregate information in a learning map and to handle information actualization and devaluation represents a major building block for future highly automated driver assistance systems.