9results about How to "Increase feature dimension" patented technology

The invention discloses an analog circuit fault diagnosis method and system for optimizing an SVM (Support Vector Machine) and a storage medium. An SVM algorithm is optimized by adopting a fused particle swarm algorithm and an improved grey wolf algorithm. Comprising the following steps: extracting time domain features and wavelet packet features of various fault sample signals of an analog circuit, and fusing the two features as a final fault feature sample set; dividing the fault feature sample set into a training set and a test set according to a certain proportion; optimizing a kernel function and a penalty factor of the SVM by using a fusion particle swarm algorithm and an improved grey wolf algorithm, and constructing a PSO-IGWO-SVM model; and training and testing the PSO-IGWO-SVM model by using the test set, and outputting a fault diagnosis result of the analog circuit. By combining the advantages of the two algorithms, the classification effect and diagnosis precision of the SVM are improved. Meanwhile, by introducing a nonlinear convergence factor and a proportional weight dynamic adjustment strategy, the search efficiency and convergence speed of the algorithm are further improved.

The embodiment of the invention discloses a skin state classification system based on multi-modal feature fusion. A specific implementation mode of the system comprises a patient state text information input device, a skin image acquisition device, a main control chip and a classification result display, wherein the patient state text information input device inputs a patient state structured text information sequence and a patient state unstructured text information sequence; the skin image acquisition device acquires a skin lesion image of a patient; the main control chip is configured to obtain an initial first state text feature vector and an initial second state text feature vector sequence; performing image feature extraction processing on the skin lesion image of the patient to obtain an initial skin lesion region feature vector; inputting the initial first state text feature vector, the initial second state text feature vector sequence and the initial skin lesion region feature vector into a skin state classification model; and the classification result display displays the skin state classification result. According to the embodiment, the classification accuracy of the skin state can be improved.

The invention discloses a transformer fault diagnosis method based on a data enhancement model and related equipment, and the method achieves the training process optimization of a CTGAN through introducing a CRS module under the condition of considering different data types of a data column, achieves the high-quality generation of DGA table data, and improves the efficiency. Evaluating the quality of the generated data by using a KS index and a CNN model; dGA data features are amplified and screened based on full-type gas ratios and a random forest algorithm, and large-scale abundant and effective screening of gas ratio features is achieved under the original background that only part of empirical features are selected. The effectiveness of the data dimension amplification method is verified by using a CNN model subsequently, and the feature expressive power is improved while feature dimension amplification is realized; through the data processing operation, the problems of poor data generation quality and low transformer fault diagnosis precision in the prior art are solved.

The application discloses a sea surface small target detection method based on multi-domain multi-dimensional feature combination, and adaptively extracts deep features of sea clutter and target echo signals by using a stack sparse auto-encoder, so that the feature dimension is improved to ensure the feature feasibility. Since the feature extraction is a complete adaptive process, the complexity of the model is reduced. Meanwhile, aiming at the problem that the feature distinction degree of the sea clutter and target echo data in a single domain is low, a time-frequency domain feature combination method is provided to improve the feature difference and ensure the stable and efficient detection performance of the detector. Through an adaptive genetic algorithm, the convergence process of the super parameter group optimization is accelerated, and the local optimization of the super parameter is prevented to a certain extent, so that the final detection probability is effectively improved. The experimental results show that the detector provided by the application has better detection effect on high sea state data, the detection probability is improved by 27.6%, and the sea surface small target detection under high sea state can be coped with.

The present application relates to a method, device, equipment and medium for detecting small traffic targets of high-speed vehicles, which relates to the field of image processing. The method includes: inputting a traffic scene image frame into a second small traffic target detection model that has been trained to a convergence state, and in a plurality of Star Block modules of a multi-layer stacked recursion of a StarNet lightweight network, using a star operation to perform element-by-element multiplication of image features corresponding to the traffic scene image frame to exponentially increase the implicit feature dimension in the traffic scene image frame to determine the high-dimensional image features of the traffic scene image frame; inputting the high-dimensional image features into a detection head network in a small traffic target detection model to obtain a plurality of small traffic targets in the traffic scene image frame to complete the small traffic target detection of high-speed vehicles. The present application can accurately and quickly detect small traffic targets in a high-speed traffic scene, and can achieve higher target detection performance in a resource-constrained environment such as a high-speed vehicle.

This invention relates to the field of deep learning technology, specifically providing a model training method, control device, and computer-readable storage medium, aiming to solve the problem of ensuring model evaluation performance while ensuring rapid model transfer. To this end, the invention obtains a trained first linear model, and creates a second linear model based on the first linear model, wherein the feature dimension of the second linear model is greater than that of the first linear model. Features are padded on a first dataset used to train the first linear model, and the padded first and second datasets are used to train the second linear model, resulting in a trained second linear model. This allows for rapid transfer based on the first linear model, and the second linear model, by using a dataset with more feature fields, achieves higher performance with higher feature dimensions.

The application provides a proppant migration state prediction method and system based on images, and relates to the technical field of oil and gas development.The method comprises the following steps: processing and predicting a real-time image sequence currently collected by using a prediction neural network model to obtain current placement form prediction data of the proppant in a complex fracture; obtaining a regulation instruction based on the current placement form prediction data, and sending the regulation instruction to a field fracturing construction control unit to close-loop control the proppant migration process.The application realizes accurate prediction and closed-loop control of the proppant migration state through downhole image collection, preprocessing, segmentation correction, multi-source data fusion modeling and real-time prediction and regulation.

This invention discloses a method for dynamic filtering and concentration prediction of drug extraction time-series signals, belonging to the field of drug signal detection technology. The method includes acquiring an original time-series signal sequence containing subsequences of pressure fluctuations, temperature drift, and conductivity changes. Dynamic noise features are extracted to separate the periodic vibration noise component of the equipment from the random noise component of fluid turbulence. An adaptive filtering kernel function is constructed and convolutionally processed to obtain the initial filtered signal. Baseline drift is corrected using a weighted baseline estimate based on the temperature drift subsequence, resulting in a dynamically corrected signal sequence. Signal feature inflection points are detected and marked, and single extraction cycle segments are segmented. Peak amplitude sequences and inter-peak time interval sequences are extracted and input into a pre-trained model to output predicted drug concentration values. This method can adaptively adapt to complex noise environments, correct signal baseline shifts, accurately divide extraction cycles, effectively mine time-series signal features, and optimize drug concentration prediction performance.

The invention belongs to the field of artificial intelligence, and relates to an audio emotion recognition method, which comprises the following steps: acquiring original audio data of a target object; preprocessing the original audio data to obtain preprocessed audio data; performing feature extraction on the audio data by adopting a pre-trained self-supervised model to obtain audio feature information of the audio data; performing dimension reduction processing on the audio feature information to obtain an emotion feature vector of the audio data; based on the emotion feature vector, performing emotion classification on the audio data by adopting a preset emotion classification model to obtain an emotion classification result; and determining an emotion type represented by the audio data according to an emotion classification result. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the original audio data can be stored in a block chain. The accuracy of audio emotion recognition can be improved.