A methodology for producing reliable software, volume 2

For abstract, see Vol. 1

A methodology for producing reliable software, volume 1

An investigation into the areas having an impact on producing reliable software including automated verification tools, software modeling, testing techniques, structured programming, and management techniques is presented. This final report contains the results of this investigation, analysis of each technique, and the definition of a methodology for producing reliable software

An economic transport system of the next generation integrating the northern and southern passages

The ice cap surrounding the Arctic Ocean has been significantly reduced during the last decades. As the ice continues to diminish the economic potential of the NSR is becoming stronger. However there are still challenges and uncertainties connected to navigation in the Arctic. Among these are the lack of marine infrastructure, the uncertainties regarding the regulations and length of the ice free season. The purpose of this master thesis is therefore to develop a transport simulation model to investigate the economic feasibility of a NSR transport system. The route has not been evaluated as a year-round substitute for the traditional route through the Suez Canal, but has been integrated with the southern passage. As a result the Northern Sea Route is only used as an alternative in the navigation season between August and the end of November. In order to investigate the feasibility of the route a case study is developed. Container cargo is evaluated as the most suitable shipping cargo; therefore the case study presents a possible container transport between Rotterdam in the Netherlands and Yokohama in Japan. The shorter distance of the NSR is exploited in two ways, either by slow steaming or increasing the number of transits a year. In addition the transport systems are evaluated for 4 different ice classes, 7 different ice scenarios and a fleet consisting of 6 or 7 vessels. The transport simulation model calculates the speed and fuel consumption in ice with the use of an ice thickness-speed curve (h-v curve). The h-v curve is found by calculating the ice resistance of the vessel for variable ice thicknesses and the corresponding net thrust available to overcome this resistance. Further the model simulates the schedules and calculates the total fuel consumption for the entire fleet. The output of the model is the required freight rate (RFR) for the NSR transport systems and the Suez Canal route.The simulation results indicate that:- The optimal fleet size consist of 7 vessels- The slow steaming schedule is more profitable than the maximum transits schedule- The optimal ice class for the less severe ice scenarios are IC, while IB is better when the ice conditions harshen- All ice classes are more profitable than the SCR if the ice conditions are less severe than ice scenario

Bayesian spatial inversion and conjugate selection Gaussian prior models

We study conjugate prior models in Bayesian spatial inversion. The spatial Kriging model may be phrased in a conjugate Bayesian inversion setting with a Gaussian prior model and a Gauss-linear likelihood function, resulting in a Gaussian posterior model. Spatial variables with unimodal, symmetric spatial histograms can be represented by this Kriging model. We generalize this Gaussian prior model by a selection mechanism, and this selection Gaussian prior model may represent multimodal, skewed, and/or peaked spatial variables. Also this selection Gaussian prior model is conjugate with respect to Gauss-linear likelihood functions. Hence the posterior model is selection Gaussian and analytically tractable. Efficient algorithms for simulation of and prediction in the selection Gaussian posterior model are defined. Model parameter inference in a maximum likelihood setting, which is simplified by the conjugate property, is also discussed. Moreover, we demonstrate that any conjugate prior model can be generalized by selection and still remain conjugate with respect to the actual likelihood function. Lastly, a seismic inversion case study is presented, and improvements of 20--40% in prediction mean-square-error, relative to traditional Gaussian inversion, are found.publishedVersio