Relaxing high-dimensional constraints in the direct solution space method for early phase development

Early phase distributed system design can be accomplished using solution spaces that provide an interval of permissible values for each functional parameter. The feasibility property guarantees fulfillment of all design requirements for all possible realizations. Flexibility denotes the size measure of the intervals, with higher flexibility benefiting the design process. Two methods are available for solution space identification. The direct method solves a computationally cheap optimization problem. The indirect method employs a sampling approach that requires a relaxation of the feasibility property through re-formulation as a chance constraint. Even for high probabilities of fulfillment, \(P>0.99\), this results in substantial increases in flexibility, which offsets the risk of infeasibility. This work implements the chance constraint formulation into the direct method for linear constraints by showing that its problem statement can be understood as a linear robust optimization problem. Approximations of chance constraints from the literature are transferred into the context of solution spaces. From this, we derive a theoretical value for the safety parameter \(\varOmega\). A further modification is presented for use cases, where some intervals are already predetermined. A problem from vehicle safety is used to compare the modified direct and indirect methods and discuss suitable choices of \(\varOmega\). We find that the modified direct method is able to identify solution spaces with similar flexibility, while maintaining its cost advantage.     

Challenges of Diffusion Bonding of Different Classes of Stainless Steels

Solid state diffusion bonding is used to produce monolithic parts exhibiting mechanical properties comparable to those of the bulk material. This requires diffusion of atoms across mating surfaces at high temperatures, accompanied by grain growth. In case of steel, polymorphy helps to limit the grain size, since the microstructure is transformed twice. The diffusion coefficient differs extremely for ferritic and austenitic phases. Alloying elements may shift or suppress phase transformation until the melting range. In this paper, diffusion bonding experiments are reported for austenitic, ferritic, and martensitic stainless steels possessing varying alloying elements and contents. Passivation layers of different compositions are formed, thus affecting the local diffusion coefficient and impeding diffusion across faying surfaces. As a consequence, different bonding temperatures are needed to obtain good bonding results, making it difficult to control the deformation of parts, since strong nonlinearities exist between temperature, bonding time, and bearing pressure. For martensitic stainless steel, it is shown that it is very easy to obtain good bonding results at low deformation, whereas ferritic and austenitic stainless steels require much more extreme bonding parameters.

     

Locating computer clubs in multicultural neighborhoods: How collaborative project work fosters integration processes

Located in socially and culturally diverse neighborhoods, we have built a network of intercultural computer clubs, called come_IN. These clubs offer a place to share practices among children and adults of diverse ethnical backgrounds. We show how this initiative ties into the striving for the integration of migrant communities and host society in Germany. In this paper, we analyze how collaborative project work and the use of mobile media and technologies contribute to integration processes in multicultural neighborhoods. Qualitative data gathered from interviews with club participants, participative observation in the computer clubs, as well as the analysis of artifacts created during project work provides the background needed to match local needs and peculiarities with (mobile) technologies. Based on these findings we present two approaches to add to the technological infrastructure: (1) a mesh-network extending the clubs into the neighborhood and (2) a project management tool, which supports projects and stimulates the sharing of ideas among projects.     

Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application

Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user-system interaction of a mobile application. In our research we designed a method that aligns the inspection method “Software ArchitecTure analysis of Usability Requirements realizatioN” SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We exemplify and apply our approach in a case study. This allows us to show how our method can be used to identify more usability problems than with each method separately. Additionally, we could confirm the validity and identified the severity of usability problems found by both methods. Our work presents how a combination of both methods allows to address usability issues in a more holistic way. We argue that the increased quantity and quality of results can lead to a reduction of the number of iterations required in early stages of an iterative software development process.     

Ceramic micro parts produced by micro injection molding: latest developments

Powder injection molding is a preferred technology for the production of micro parts or microstructured parts. Derived from the well known thermoplastic injection molding technique it is suitable for a large-scale production of ceramic and metallic parts without final machining. To achieve good surface quality and control the part size and distortions is an important goal to allow mass production. This means that all process steps like part design adjusted for MIM/CIM-technology, appropriate choice of powder and binder components and injection molding simulation to design the sprue are required. Concerning the injection molding itself high quality mold inserts, high-precision injection molding with suitable molding machines like Battenfeld Microsystem50 or standard machine with special equipment like variotherm or evacuation of the molding tool and an adjusted debinding and sintering process have to be available. Results of producing micro parts by powder injection molding of ceramic feedstock will be presented.     

Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.     

Scaling Kernel-Based Systems to Large Data Sets

In the form of the support vector machine and Gaussian processes, kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the size of the training data set, such that these systems are not ideal candidates for applications with large data sets. Nevertheless, research in this direction is very active. In this paper, I review some of the current approaches toward scaling kernel-based systems to large data sets.