This study constructs an original mathematical model of a shipboard container crane and proposes a nonlinear controller for the complicated operation duties in which the viscoelasticity of seawater and the flexibility of handling cable are taken into account. By using two inputs, namely, the pulling force of the trolley and the torque of the hoist, the controller simultaneously drives six outputs, including trolley motion, cable length, container swing, axial container oscillation, ship roll, and ship heave. The effects of elasticity factors and wave excitations on system performance are also investigated. The simulation and experiment results reveal that the controlled system responses remain stable and consistent despite disturbances.
Existing work on domain adaptation for statistical machine translation has consistently assumed access to a small sample from the test distribution (target domain) at training time. In practice, however, the target domain may not be known at training time or it may change to match user needs. In such situations, it is natural to push the system to make safer choices, giving higher preference to domain-invariant translations, which work well across domains, over risky domain-specific alternatives. We encode this intuition by (1) inducing latent subdomains from the training data only; (2) introducing features which measure how specialized phrases are to individual induced sub-domains; (3) estimating feature weights on out-of-domain data (rather than on the target domain). We conduct experiments on three language pairs and a number of different domains. We observe consistent improvements over a baseline which does not explicitly reward domain invariance.
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