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Traditional trailing suction hopper dredgers (TSHDs) are generally equipped with diesel engines as single power source. The diesel engines must be sized to cater for the maximum power demand, so they are significantly oversized most of the time. To solve this problem, an optimal retrofitting concept is proposed for matching main power equipments in hybrid propulsion system for a TSHD. The nondominated sorting genetic algorithm II (NSGA-II) is adopted to optimize the hybrid propulsion system design. Power equipments here include main engine, Variable Speed Drive (VSD), battery bank. The optimization is concerned with minimization of total installation weight and fuel consumption, also with limiting the value of State of Charge (SOC) by using graduated system of punishment. The different solutions are reflected in final Pareto front.

The paper introduces the development procedure of rapid control prototype in marine electric propulsion system, concrete implementation in each subsystem of hull-propeller-engine model by Matlab/SIMULINK, building process of hardware-in-the-loop simulation system and procedure of developing real-time system by dSPACE. The key questions and technical essentials involved in the hardware-in-the-loop simulation of marine electric propulsion system are introduced briefly. The whole design of the systemic technical scheme, the selection and control of the controller and processor, the work principle of the hardware-in-the-loop simulation and the result of simulation are also involved.

In order to obtain a better human-machine engineering implementation effect and get a faster feedback during the bridge designing, the paper suggested a virtual simulation and evaluation method. And by using of the software Visual C++, MultiGen and Vega, an evaluation simulation system was also built. Such a system can display the whole layout of the bridge, evaluate the key human-machine designing elements and do real-time simulation. It can also provide decision supporting to the designers. Through actual usage proof, the method is quite availability and has practical value.

The paper mainly probes into the accuracy of applying the Region growth algorithm to MRI images segmentation. Firstly the researchers scan the adult volunteers to get stereotaxtic MRI images, then discern and segment them in two separate ways: Region growth algorithm and manual segmentation, and compare the differences in results. The outcome shows no obvious difference exists, so the Region growth algorithm can be used into the segmentation of some complex formations such as hippocampus.