Abstract: The present disclosure discloses a method for detecting a natural frequency of a blade by a single blade tip timing sensor. In the method, two segments of displacement data vectors are subjected to dot product to obtain a product data vector after multiplication of corresponding serial numbers, the product data vector is subjected to low-frequency filtering, the filtered product vector is subjected to Hilbert transform to obtain an instantaneous phase, the instantaneous phase is subjected to discrete Fourier transform, an absolute value of a natural frequency difference between blades is extracted from amplitude frequency data, blades on a bladed disk are subjected to permutation and combination to obtain absolute values of the natural frequency differences between all the blades, a sum of the absolute values of the natural frequency differences between each blade and other blades is calculated.

Abstract: The present disclosure discloses a method for extracting a natural frequency difference between blades by a single blade tip timing sensor or uniformly distributed blade tip timing sensors. The method includes the following steps: acquiring actual arrival time of a rotating blade by using a single blade tip timing sensor or uniformly distributed blade tip timing sensors, and converting a difference between theoretical arrival time and the actual arrival time into displacement data of a blade tip according to a rotational speed and a blade length of the rotating blade; selecting displacement data of blade tips of two rotating blades with the same blade length at the same rotational speed; intercepting the displacement data, performing discrete Fourier transform respectively, and making a sampling frequency approximate to an average rotational speed to obtain spectrum data.

Abstract: In sequence level prediction of a sequence of frames of high dimensional data one or more affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated affective labels.

Abstract: In sequence level prediction of a sequence of frames of high dimensional data one or more affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated affective labels.

Abstract: Methods and systems for performing sequence level prediction of a video scene are described. Video information in a video scene is represented as a sequence of features depicted each frame. One or more scene affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames of data. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated scene affective labels.

Abstract: Methods and systems for performing sequence level prediction of a video scene are described. Video information in a video scene is represented as a sequence of features depicted each frame. One or more scene affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames of data. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated scene affective labels.

Abstract: Methods and systems for performing sequence level prediction of a video scene are described. Video information in a video scene is represented as a sequence of features depicted each frame. An environment state for each time step t corresponding to each frame is represented by the video information for time step t and predicted affective information from a previous time step t?1. An action A(t) as taken with an agent controlled by a machine learning algorithm for the frame at step t, wherein an output of the action A(t) represents affective label prediction for the frame at the time step t. A pool of predicted actions is transformed to a predicted affective history at a next time step t+1. The predictive affective history is included as part of the environment state for the next time step t+1. A reward R is generated on predicted actions up to the current time step t, by comparing them against corresponding annotated movie scene affective labels.

Abstract: Methods and systems for performing sequence level prediction of a video scene are described. Video information in a video scene is represented as a sequence of features depicted each frame. An environment state for each time step t corresponding to each frame is represented by the video information for time step t and predicted affective information from a previous time step t?1. An action A(t) as taken with an agent controlled by a machine learning algorithm for the frame at step t, wherein an output of the action A(t) represents affective label prediction for the frame at the time step t. A pool of predicted actions is transformed to a predicted affective history at a next time step t+1. The predictive affective history is included as part of the environment state for the next time step t+1. A reward R is generated on predicted actions up to the current time step t, by comparing them against corresponding annotated movie scene affective labels.