2 results about "Nearest neighbor classifier" patented technology

A KNN-based lateral stability control method for heavy-duty AGVs is disclosed, characterized by the following steps: 1. Classifying AGV operating conditions into five categories based on different loads, and collecting motion state data of the AGV under different operating conditions using simulation software; 2. Establishing a K-nearest neighbor (KNN) classifier, and training and validating it using the collected dataset; 3. Designing a set of nonlinear sub-controllers based on fuzzy PID, calculating the required yaw moment, and distributing the torque to the four drive wheels according to the torque distribution rule; 4. Introducing an error judgment strategy, activating the controller based on the centroid sideslip angle error to control its lateral stability. This invention establishes a load KNN classifier for heavy-duty AGVs, which can monitor the centroid sideslip angle error in real time. If the error exceeds a threshold, the controller is activated, the classifier's classification result is matched to the corresponding sub-controller, and an additional yaw moment is calculated and applied to the AGV.

The purpose of this invention is to address the problems of poor adaptability to multiple postures, distortion of the combined acceleration waveform, and insufficient accuracy caused by fixed thresholds in existing smartphone gait detection technologies. It relates to the fields of pedestrian dead reckoning and motion perception technology, and provides a smartphone multi-posture gait detection method based on a dual adaptive mechanism, comprising the following steps: acquiring three-axis data from the smartphone accelerometer; calibrating noise parameters using Allan variance and constructing a Kalman filter for noise reduction; extracting temporal features and identifying the phone's carrying posture in real time using a K-nearest neighbor classifier; entering the evaluation stage, calculating the peak-normalized fluctuation coefficient and trough-normalized fluctuation coefficient of the three-axis acceleration signal respectively, and selecting the axis with the smallest total fluctuation coefficient as the optimal detection axis. This invention effectively solves the industry problems of low accuracy and poor robustness in gait recognition under multiple smartphone carrying conditions.