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To improve surplus torque suppression and loading performance of electric load simulators, this paper presents a loading control strategy based on the new mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller. The proposed mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller, designed free from the mathematical model of system, comprises a mapping fuzzy Cerebellar Model Articulation Controller and a fuzzy inference controller, in which the former is the main controller. By introducing the new mapping approach in mapping fuzzy Cerebellar Model Articulation Controller, the proposed control strategy is actually a global network with local weight updating and its continuity has been enhanced. The fuzzy inference controller is used as a fuzzy compensator. As a torque controlled system, electric load simulator takes the loading error as the performance index. The results of dynamic simulation and experiments indicate that the proposed loading control strategy can achieve favorable control performance.

This paper proposes a dual-typed and omnidirectional infrared perceptual network for indoor human target location and tracking. Two types of infrared sensors, pyroelectric infrared sensors and thermopile array sensors, are used in the sensor network for side-view and top-view perception respectively. The sensor nodes are deployed to construct an omnidirectional sensing model for detecting human targets in an indoor scenario with irregular boundary, furniture and other obstacles. The improved credit location algorithm and the adaptive threshold algorithm are applied for the human location. The location ambiguity existing in the actual situations is analysed and the compensation methods are designed by using an equivalent measurement line and a state machine to reduce the location ambiguity. Finally, a fuzzy adaptive CS-jerk (current statistical-jerk) algorithm is applied for tracking the human target. The tracking results indicate that the dual-typed and omnidirectional system works well in the indoor environment.

Compared with the general Visual Question Answering (VQA), Medical VQA is more challenging. Medical images contain more complex information than general images. Aiming at this point, we propose the IIF module that can improve the model's ability to obtain visual feature. In addition, we design QAM to help the model analyze the question better. On the VQA-RAD dataset, the accuracy of our model improved to 66.4% on the opened-ended questions and 80.1% on the closed-ended questions, outperforming other relevant models. The results on the VQA-MED 2019 dataset also verify the effectiveness of our model.