A handheld 3D laser scanning system is proposed for measuring large-sized objects on site. This system is mainly composed of two CCD cameras and a line laser projector, in which the two CCD cameras constitute a binocular stereo vision system to locate the scanner’s position in the fixed workpiece coordinate system online, meanwhile the left CCD camera and the laser line projector constitute a structured light system to get the laser lines modulated by the workpiece features. The marked points and laser line are both obtained in the coordinate system of the left camera in each moment. To get the workpiece outline, the handheld scanner’s position is evaluated online by matching up the marked points got by the binocular stereo vision system and those in the workpiece coordinate system measured by a TRITOP system beforehand; then the laser line with workpiece’s features got at this moment is transformed into the fixed workpiece coordinate system. Finally, the 3D information composed by the laser lines can be reconstructed in the workpiece coordinate system. A ball arm with two standard balls, which is placed on a glass plate with many marked points randomly stuck on, is measured to test the system accuracy. The distance errors between the two balls are within ±0.05 mm, the radius errors of the two balls are all within ±0.04 mm, the distance errors from the scatter points to the fitted sphere are distributed evenly, within ±0.25 mm, without accumulated errors. Measurement results of two typical workpieces show that the system can measure large-sized objects completely with acceptable accuracy and have the advantage of avoiding some deficiencies, such as sheltering and limited measuring range.

High-frequency surface wave radar (HFSWR) can detect and continuously track ship objects in real time and beyond the horizon. When ships navigate in a sea area, their motions in a time period form a scenario. The diversity and complexity of the motion scenarios make it difficult to accurately track ships, in which failures such as track fragmentation (TF) are frequently observed. However, it is still unclear how and to what degrees the motions of ships affect the tracking performance, especially which motion patterns can cause tracking failures. This paper addresses this problem and attempts to undertake a first step towards providing an intensive quantitative performance assessment and vulnerability detection scheme for ship-tracking algorithms by proposing an evolutionary and data-mining-based approach. Low-dimensional scenarios in terms of multiple maneuvering ship objects are generated using a grammar-based model. Closed-loop feedback is introduced using evolutionary computation to efficiently collect scenarios that cause more and more tracking performance loss, which provides diversified cases for analysing using data-mining technique to discover indicators of tracking vulnerability. Results on different tracking algorithms show that more cluster and convergence patterns and longer duration of our convoy and cluster patterns in the scenarios can cause severer TF to HFSWR ship tracking.

Stories are useful tools with which we can exchange experience learnt in social contexts, ways to communicate futures in strategic planning, and unique building blocks that connect meanings in a movie or a virtual environment. Evolutionary computation (EC) techniques have the potential to overcome existing limitations in automated storytelling, whereby evolution can provide a process of innovation. However, one source of complexity lies in the transformation of a story in a natural language into a representation that EC can evolve easily. Another complexity arises from the fact that the ultimate judge for the quality of a story is a human–being, and humans are diverse in their taste. This paper attempts to tackle the above complexities through an automatic story narration application. We present a methodology which can transform a story written in English into an event-level and hierarchical level grammar using a network representation. This approach makes it possible to devise an encoding scheme that translates a story narration with flashback into a chromosome and vice versa. We then discuss different metrics for the evolutionary narration problem and use 42 human participants to evaluate the generated narrations. To incorporate diversified human opinions, we propose to build individual human surrogate models from the human evaluation experiment and further fuse them into an ensemble. The ensembles of human surrogate models serve as the objective functions of multi-objective EC to guide the generation of desirable stories from human perspectives. We demonstrate that this approach is successful in evolving better narrations as assessed by 31 human participants.