Search

The Self-Enforcing Network (SEN), which is a self-organized learning neural network, is introduced as a tool for clustering to define reference types in complex data. In order to achieve this, a cue validity factor is defined, which first steers the clustering of the data. Finding reference types allows the analysis and classification of new data. The results show that a user can influence the clustering of data by sEN, thus allowing the analysis of the data depending on specific interests. The described tool includes concrete examples with real clinical data and shows the potential of such a network for the analysis of complex data.

Lexical recognition tests are widely used to assess vocabulary knowledge. We investigate the role that diacritics play in designing an Arabic lexical recognition test. We compare a non-diacritized and a diacritized test in a user study and find that they are largely comparable in their ability to assess vocabulary proficiency. However, we argue that diacritized tests are better suited to control the test difficulty by allowing better nonwords and a more targeted selection of word forms.

For developing nations, where mixed traffic conditions prevail, it becomes necessary to develop a heterogeneous traffic flow model which is able to model all vehicle types of different static and dynamic characteristics and equally efficient in computational performance. For this purpose, in this study, an agent based framework is used which uses a queue model in its mobility simulation. In first step, queue model is enhanced by adding different vehicle types with different maximum speeds and sizes. Furthermore, traditional FIFO (First-In-First-Out) approach of queue model is modified to a more realistic modified queue model in which faster vehicles can pass slower vehicles. The enhancements are discussed with their fundamental diagrams for traffic flow and spatio-temporal diagrams.

This paper investigates the closure of TXL airport and its impact on the bus network of Berlin. The results of the scenario are based on a co-evolutionary algorithm for public transit network design. The algorithm is integrated in a multi-modal multi-agent simulation. In the simulation, competing minibus operators start exploring the public transport market offering their services. With more successful operators expanding and less successful operators going bankrupt, a sustainable network of minibus services evolves. In the TXL scenario, the impact of the massive change in demand is found to be locally confined. Only transit lines serving TXL airport directly are affected. Furthermore, transit lines are found to have a higher probability of surviving if connecting two different activity centers, e.g. transit hubs. Following a hub-and-spoke approach by letting the line end in low-demand areas renders a line less attractive because of a reduced connectivity, e.g. to one train station only.

There are strong interactions between energy consumption at home, in the office, and in the traffic system. For example, workers can telecommute, saving the energy at the office and in the transport system, but on the other hand increasing energy consumption at home. As an alternative measure, making working hours less flexible and thus forcing everybody to be at the workplace at the same time reduces the energy consumption of the office building. Both measures in addition have traffic congestion consequences. This paper discusses these policies based on a simple analytical model as well as based on a simulation model which includes a sophisticated simulation of the transport system.

Usability is crucial for the user acceptance of software products. Some usability requirements influence software architecture and therefore should be considered early in the software development process. In this short paper, we propose the design of generic interaction scenarios that are mined from best practices (patterns). Our scenarios describe a user or system interaction as stimulus, the response of the system, and its assessment. We classified them in order to provide them in a knowledge base, as they are used for scenario-based analyses that assess usability properties of mobile applications. © 2011 Published by Elsevier Ltd.

We investigate replacing travel diaries with sets of call detail records (CDRs) as input data for an agent-oriented traffic simulation. Synthetic CDRs are used in order to study the effect of this substitution in isolation. We introduce an experimental design where a detailed synthetic transportation scenario with individual simulated travellers is combined with a simple model of mobile phone usage to collect synthetic CDRs. This set of artificial CDRs is then considered as input for another instance of the same traffic model, disregarding all other information. We analyse to what degree the model reproduces the base case, depending on the frequency of the available CDRs.

Sensors, actuators and devices that compose the Internet of Things (IoT) world are becoming more diverse every day in terms of capabilities and amount of generated traffic. Current Machine-to-Machine (M2 M) communication standardization efforts try to formalize the interfaces between M2 M nodes based on the perspective of exchanging uniform small data size with low sampling rate only. However, many devices will require support for more heterogeneous traffic patterns, with different network capacity. This paper introduces a communication concept for supporting gracefully a heterogeneous set of devices. This paper analyses the effect of traffic size in M2 M transactions and propose a concept to adapt gracefully to support heterogeneous traffic patterns in M2 M systems. To prove its feasibility, the concept is exemplified on top of oneM2 M architecture and implemented as part of the Fraunhofer FOKUS OpenMTC toolkit. Additionally, the concept was applied to a deployment in an E-Health pilot and practical measurements during functional evaluation are reported.