Registration And Feature Extraction From Terrestrial Laser Scanner Point Clouds For Aerospace Manufacturing

Aircraft wing manufacture is becoming increasingly digitalised. For example, it is becoming possible to produce on-line digital representations of individual structural elements, components and tools as they are deployed during assembly processes. When it comes to monitoring a manufacturing environment, imaging systems can be used to track objects as they move about the workspace, comparing actual positions, alignments, and spatial relationships with the digital representation of the manufacturing process. Active imaging systems such as laser scanners and laser trackers can capture measurements within the manufacturing environment, which can be used to deduce information about both the overall stage of manufacture and progress of individual tasks. This paper is concerned with the in-line extraction of spatial information such as the location and orientation of drilling templates which are used with hand drilling tools to ensure drilled holes are accurately located. In this work, a construction grade terrestrial laser scanner, the Leica RTC360, is used to capture an example aircraft wing section in mid-assembly from several scan locations. Point cloud registration uses 1.5"white matte spherical targets that are interchangeable with the SMR targets used by the Leica AT960 MR laser tracker, ensuring that scans are connected to an established metrology control network used to define the coordinate space. Point cloud registration was achieved to sub-millimetre accuracy when compared to the laser tracker network. The location of drilling templates on the surface of the wing skin are automatically extracted from the captured and registered point clouds. When compared to laser tracker referenced hole centres, laser scanner drilling template holes agree to within 0.2mm

Richer concepts are better remembered: number of features effects in free recall

Many models of memory build in a term for encoding variability, the observation that there can be variability in the richness or extensiveness of processing at encoding, and that this variability has consequences for retrieval. In four experiments, we tested the expectation that encoding variability could be driven by the properties of the to-be-remembered item. Specifically, that concepts associated with more semantic features would be better remembered than concepts associated with fewer semantic features. Using feature listing norms we selected sets of items for which people tend to list higher numbers of features (high NoF) and items for which people tend to list lower numbers of features (low NoF). Results showed more accurate free recall for high NoF concepts than for low NoF concepts in expected memory tasks (Experiments 1–3) and also in an unexpected memory task (Experiment 4). This effect was not the result of associative chaining between study items (Experiment 3), and can be attributed to the amount of item-specific processing that occurs at study (Experiment 4). These results provide evidence that stimulus-specific differences in processing at encoding have consequences for explicit memory retrieval

An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition

There is considerable evidence (e.g., Pexman et al., 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using mixed effects analyses to control for the influence of correlated lexical variables, we considered the impact of number of features, number of senses, semantic neighborhood density, imageability, and body–object interaction across five visual word recognition tasks: standard lexical decision, go/no-go lexical decision, speeded pronunciation, progressive demasking, and semantic classification. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing

Context matters: How do task demands modulate the recruitment of sensorimotor information during language processing?

Many theories of semantic representation propose that simulations of sensorimotor experience contribute to language processing. This can be seen in the body-object interaction effect (BOI; how easily the human body can interact with a word’s referent). Words with high BOI ratings (e.g., ball) are processed more quickly than words with low BOI ratings (e.g., cloud) in various language tasks. This effect can be modulated by task demands. Previous research established that when asked to decide if a word is an object (entity condition), a BOI effect is observed, but when asked to decide if a word is an action (action condition), there is no BOI effect. It is unclear whether the null behavioral effect in the action condition reflects top-down modulation of task-relevant sensorimotor information or the absence of bottom-up activation of sensorimotor simulations. We investigated this question using EEG. In Experiment 1 we replicated the previous behavioral findings. In Experiment 2, 50 participants were assigned to either the entity or action conditions and responded to the same word stimuli. In both conditions we observed differences in ERP components related to the BOI effect. In the entity condition the P2 mean amplitude was significantly more positive for high compared to low BOI words. In the action condition the N400 peak latency was significantly later for high compared to low BOI words. Our findings suggest that BOI information is generated bottom-up regardless of task demands and modulated by top-down processes that recruit sensorimotor information relevant to the task decision

Grasping the Alternative: Reaching and Eyegaze Reveal Children’s Processing of Negation

There is evidence that children begin to understand negation early in the preschool years, but children’s processing of negation is not well understood. We examined children’s processing of denial negation using a variant of the visual world paradigm called the Shopping Task. In this task, participants help a puppet to find the items on a shopping list, selecting from two potential items on each trial in response to the puppet’s affirmative (“the next item is an apple”) or negation (“the next item is not an orange”) sentence. In this binary decision context, participants’ eye gaze and reaching behavior were tracked as they selected the item the puppet wants. Participants were 78 children aged 4–5 years and a comparison group of 30 adults. Results showed that children took longer to process negation than affirmative sentences, and that this difference arose early in processing. Further, children’s eye gaze behavior suggested that on negation trials they regularly looked first to the negated object and were considering the negated meaning early in processing. Adults did not take longer to process negation than affirmative sentences, but their eye gaze behavior also indicated early consideration of negated meanings for negation sentences. We also examined relationships between children’s language and executive function skills and their processing of negation and found no significant relationships. We conclude that both adults and children activate to-be-negated information in the processing of negation. Children, however, are less efficient at processing negation in this context