Abstract: Repulsive force created by actuated permanent magnets is used to levitate and transport heavy loads. A bed of permanent magnets is selectively actuated to levitate an array of magnets positioned above the bed, such that the magnets in the levitated array are opposed to the actuated magnets, and of the same magnetic pole, thereby creating a repulsive force. The actuated magnets are vertically offset from magnets in the bed of permanent magnets that have not been raised, thereby imparting maximum levitation forces to the magnets in the levitated array. These systems can levitate and transport objects over level or sloped surfaces, in a straight path or along curves and corners. A bed of magnets can be attached to the floor, or to a set of moving decks that rearrange themselves in a desired path. Our systems can simulate walking or running, similar to a treadmill or virtual gaming platform.

Abstract: We use permanent magnets to levitate and transport heavy loads. A bed of permanent magnets is selectively actuated to levitate an array of magnets positioned above the bed, such that the magnets in the levitated array are opposed to the actuated magnets, and of the same magnetic pole, thereby creating a repulsive force. The actuated magnets are offset from magnets in the bed of permanent magnets that have not been actuated, thereby imparting maximum levitation forces to the magnets in the levitated array. Our systems use magnetic repulsive force for levitating and transporting goods across a warehouse, simulating walking or running such as on a treadmill or in a virtual gaming platform, and for transporting people such as on a moving sidewalk. Our systems are electrical machines for holding or levitating devices using magnetic levitation, and also use permanent magnets to transmit power wirelessly.

Abstract: A machine learning system may be deployed with a less-than-optimal classification system, but may include a human in the loop system to rapidly assist in classification and deployment. The human's input may be returned as a response to a query and may also be stored for re-training the machine learning system. With a rapid human response, a machine learning system may be deployed and may “learn” over time. A multi-stage human intervention system may have a rapid response human interface, and if the first human encounters ambiguity, the request may be elevated to a second stage human expert for resolution. Such a system may be deployed using a generic or semi-generic classification system, and as the human responses are accumulated, the machine learning system may be repeatedly re-trained to reach a desired performance level.