40results about How to "Increase training rate" patented technology

The invention discloses an insulator category detection method based on deep transfer learning, comprising: 1. Preprocessing the aerial insulator image; 2. Expanding the preprocessed aerial insulator image and classifying different types of aerial insulator images ;3. Use the YOLO algorithm to initially locate the aerial insulator images with complex backgrounds, and perform normalization processing on the positioned insulators; 4. Construct an Inception deep learning network with a multi-level differential adaptive architecture; 5. Construct test samples 6. Construct an insulator state cognitive feedback adjustment mechanism based on semantic error entropy. Through the method of deep transfer learning, the present invention can realize the self-optimization adjustment and reconstruction of the multi-level differentiated feature space of the insulator state and its classification criteria, thereby improving the state detection rate of the self-explosion of the aerial image of the insulator under different backgrounds, and meeting the requirements of accuracy and speed actual needs.

The invention discloses a method for identifying deformation faults of a skirt plate grille of a railway motor car, which solves the problem of low detection efficiency of the existing method for identifying deformation faults of the skirt plate grille of a railway motor car, and belongs to the technical field of fault identification of a railway motor car. The invention includes: constructing a skirt plate grid deformation sample set of a railway motor car; using the skirt plate grid deformation sample set to train a deep learning target detection network Faster R-CNN to obtain a Faster R-CNN detection model and weight; using the Faster R-CNN ‑CNN detection model and weights identify the side image of the railway train to be detected, and determine whether the skirt grille in the side image is deformed and the position of the deformation. The feature extraction network of the present invention includes: replacing the 3x3 convolution kernel in the Bottleneck block in the Resnet-50 network with a 3x1 convolution kernel connected in series with a 1x3 convolution kernel, and replacing the ReLU activation in the feature extraction network with the Swish activation function function. The invention recognizes and detects deformation faults of train skirt plate grids, and effectively avoids recognition errors caused by fatigue and individual judgment differences during manual detection.

The invention discloses an intelligent rotating machinery fault depth network feature identification method. By arranging a vibration sensor at the part of the rotating machinery to be detected in the train rolling bearing, the original vibration sequence of the rolling bearing is collected, and then the original vibration sequence is decomposed by a singular spectrum analysis method. Reconstruct and extract the root mean square value, standard deviation, skewness index and peak value of the reconstructed vibration sequence, use the rotating machinery fault location diagnosis model trained by the support vector machine to judge the fault location, and then assemble the reconstructed vibration sequence Empirical mode decomposition calculates the permutation entropy values ​​of a set of decomposed intrinsic modal components, uses the permutation and combination of permutation entropy values ​​as detection features, and uses the rotating machinery fault type diagnosis model trained by support vector machine to judge the fault type. The invention can detect the fault position and fault type of the rotary machine in time, and improves the accuracy and reliability of fault diagnosis.

The embodiments of the present specification provide a method and device for asking a rhetorical question based on a dialogue system, the dialogue system includes a reinforcement learning model, and the method includes: acquiring a first dialogue round based on the dialogue system, the first dialogue round A dialogue round includes T rounds of dialogue; acquire T states corresponding to the first dialogue round; acquire a plurality of feedback score vectors corresponding to multiple rhetorical questions included in the T round of dialogue, the feedback score The multiple dimensions included in the vector correspond to multiple predetermined features respectively; based on the T states, the multiple rhetorical questions, and the multiple feedback score vectors, the reinforcement learning model is trained to be used based on the reinforcement The learning model asks rhetorical questions to user questions.