Abstract: The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer; and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process.

Abstract: A laterally diffused metal oxide semiconductor device and a preparation method thereof are disclosed. The semiconductor device includes: a substrate; a body region having a first conductivity type and formed in the substrate; a drift region, having a second conductivity type, formed in the substrate and adjacent to the body region; a field plate structure, formed on the drift region, a lower surface of an end of the field plate structure close to the body region being flush with the upper surface of the substrate, and the end of the field plate structure close to the body region also having an upwardly extending inclined surface; and a drain region, having a second conductivity type, formed in an upper layer of the drift region, and in contact with the end of the field plate structure away from the body region.

Abstract: Systems are methods are provided for processing multiple input objectives by a reinforcement learning agent. The method may include: instantiating a reinforcement learning agent that maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating task requests; receiving a plurality of input data representing a plurality of user objectives associated with a task request and a plurality of weights; generating a plurality of preferences based on the plurality of user objectives and the plurality of weights; computing a plurality of loss values; computing a plurality of first gradients based on the plurality of loss values; for a plurality of pairs of references, computing a plurality of similarity metrics; computing an updated gradient based on the first gradients and the plurality of similarity metrics; and updating the reinforcement learning neural network based on the updated gradient.

Abstract: Systems are methods are provided for processing multiple input objectives by a reinforcement learning agent. The method may include: instantiating a reinforcement learning agent that maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating task requests; receiving a plurality of input data representing a plurality of user objectives associated with a task request; generating, based on the reinforcement learning neural network and the plurality of input data, an action output for generating a signal for communicating the task request; computing a reward based on the action output and the plurality of input data; and updating the reinforcement learning neural network based on the reward.

Abstract: A cordless indicia reader including a multifunction coil that can be configured to either transmit or receive electromagnetic energy is disclosed. In this way, the multifunction coil facilitates both the wireless charging of a battery and the wireless deactivation of electronic article surveillance (EAS) tags. The multifunction coil and a plurality of modules to perform these functions are integrated within the cordless indicia reader's hand-supportable housing.

Abstract: A cordless indicia reader including a multifunction coil that can be configured to either transmit or receive electromagnetic energy is disclosed. In this way, the multifunction coil facilitates both the wireless charging of a battery and the wireless deactivation of electronic article surveillance (EAS) tags. The multifunction coil and a plurality of modules to perform these functions are integrated within the cordless indicia reader's hand-supportable housing.

Abstract: A cordless indicia reader including a multifunction coil that can be configured to either transmit or receive electromagnetic energy is disclosed. In this way, the multifunction coil facilitates both the wireless charging of a battery and the wireless deactivation of electronic article surveillance (EAS) tags. The multifunction coil, and a plurality of modules to perform these functions, are integrated within the cordless indicia reader's hand-supportable housing.

Abstract: A cordless indicia reader including a multifunction coil that can be configured to either transmit or receive electromagnetic energy is disclosed. In this way, the multifunction coil facilitates both the wireless charging of a battery and the wireless deactivation of electronic article surveillance (EAS) tags. The multifunction coil, and a plurality of modules to perform these functions, are integrated within the cordless indicia reader's hand-supportable housing.