An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
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Advanced operated-work machines, which have been designed for complicated tasks and which have complicated operating systems, requires intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification, which is a key technology for an intelligent system, difficult. We therefore defined primitive static states (PSS) that are determined using on-off information for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and variation in operator skill level that can cause an incorrect work state identification. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the results of PSS-based work analysis. We also confirmed in skill-training experiments that advice information based on PSS-based skill analysis greatly improved operator's work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.