189 results about "Bayesian optimization" patented technology

The invention discloses a non-contact driver fatigue detection method based on a millimeter-wave radar. A millimeter-wave radar is used to collect a thoracic cavity vibration signal of a driver in a driving process; after the thoracic cavity vibration signal is preprocessed, the heart rate signals and respiratory signals of different frequency bands are separated; the heartbeat frequency and the respiratory frequency of the separated heart rate and respiratory signals are calculated by adopting a wavelet transform method, and seven derived physiological characteristics are calculated; by exploring the change rule of the physiological features along with time, the physiological features are found to show a good linear change trend along with accumulation of driving time; and the random forest algorithm based on Bayesian optimization effectively discriminates occurrence of fatigue time, and algorithm accuracy can be improved compared with algorithm accuracy of an original random forest model. According to the invention, the discomfort brought to a driver when the driver wears various devices to carry out physiological signal detection is solved, detection cost is reduced, the fatiguemoment of the driver can be accurately predicted, fatigue early warning is sent to the driver, and therefore, the traffic accident rate caused by fatigue is reduced.

The invention relates to a machine learning super-parameter optimization system and a machine learning super-parameter optimization method based on asynchronous Bayesian optimization, comprising a Bayesian optimization module, a model parameter pool model, a Kmeans clustering module, a task scheduling module and an adaptive determination model parallelism module. The invention automatically adjusts the parameters of machine learning under the big data environment efficiently, effectively utilizes the parallel computing ability of multiple computers, and efficiently adjusts the parameters of the big data machine learning automatically, so that people can better use the big data machine learning in the production practice.

The invention relates to an intelligent automobile rapid test method based on Bayesian optimization. According to the method, representative test scenes are accurately selected, the test frequency isreduced, and the safety of a large number of samples is evaluated. The method comprises the following steps: firstly, obtaining driving scene key parameters of a vehicle on the basis of on-site traffic scenes, determining a value range and a sampling interval for the key parameters, and combining the key parameters to form a parameter space; then, based on the Bayesian optimization theory, selecting appropriate classifiers and acquisition functions according to different test purposes; and finally, initializing a classifier, calculating a numerical value of an acquisition function, and selecting a next intelligent automobile test scene which is more in line with requirements according to the numerical value of the acquisition function. Compared with the prior art, the method has the advantages of reducing the test times, improving the test efficiency, ensuring the test reliability and the like.

The invention belongs to the image recognition field and relates to a Bayesian optimization-based image table character segmentation method. The method includes the following steps that: cells in an image table to be recognized are detected; information content in each cell is segmented out wholly; spare segmentation points of characters are found for character information in each cell through using a projection method; a Bayesian classifier is utilized to judge the spare segmentation points, so that accurate segmentation points of the character information can be found out; and segmented character sub images are classified. With the Bayesian optimization-based image table character segmentation method adopted, favorable conditions are created for the accurate recognition of the character information; the completeness and accuracy of single segmented characters can be ensured; the problem of difficulty in image table segmentation in the image and character recognition field which has been difficult to be solved can be solved. The Bayesian optimization-based image table character segmentation method has very high accuracy in image table character segmentation and has a bright application prospect in the image and character recognition, information mining and information analysis field.

The invention discloses a water chilling unit fault diagnosis method and system based on Bayesian optimization LightGBM, and a medium. The method comprises the following steps: collecting and storing on-site historical data of a water chilling unit through a sensor; preprocessing the historical data; performing feature selection by using a two-step method combining an embedding method and a recursive feature elimination method; using the historical data subjected to data preprocessing and feature selection for training a LightGBM model, combining a Bayesian optimization algorithm with a ten-fold cross validation mode to determine an optimal hyper-parameter combination of the LightGBM model, and then obtaining a trained LightGBM diagnosis model; and preprocessing the real-time operation data and inputting the data into the diagnosis model to obtain a water chilling unit fault diagnosis result. Parameters of the diagnosis model can be rapidly determined, the operation state of the water chilling unit can be rapidly and accurately evaluated, key fault features can be extracted, and the method is used for guiding engineering practice and strengthening on-site operation maintenance of the water chilling unit.

The invention relates to the technical field of infrared spectroscopy wavelength, and more specifically, relates to a novel infrared spectroscopy wavelength selection method based on sparse Bayesian study. The sparse optimized wavelength selection method utilizes the spectral structure priori knowledge and the related priori knowledge of co-linearity between spectrums. The provided method has the advantages of little calculation quantity, few adjustable parameters, and strong robustness. For the first time, the spectral structure priori knowledge and the related priori knowledge of co-linearity between spectrums are utilized, and the spectral partitioned sparse structure can be determined in a self-adapting mode. Then sparse Bayesian study algorithm is adopted to calculate out the optimal solution of the sparse optimization problem so as to screen out the best wavelength point combination. The provided method can be widely applied to the field of solid-phase / liquid-phase / gas-phase infrared spectrum wavelength selection.

One embodiment of a method includes determining, by a Bayesian optimizer, a first sparsity ratio associated with a limit on an accuracy loss caused by compressing the machine learning model. The method further includes selecting, by the Bayesian optimizer, a second sparsity ratio that optimizes a predefined objective function for the machine learning model within a search space bounded by the first sparsity ratio. The method further includes generating a compressed version of the machine learning model having the second sparsity ratio.

The invention discloses a machine learning hyper-parameter adjusting method and system, relating to the technical field of machine learning. According to the invention, a Bayesian optimization framework is adopted, a prior probability model is generated by a Gaussian process through an initial observation set, a next evaluation point is searched by using a chaotic particle swarm method, a new evaluation point is evaluated to obtain an observation value corresponding to the new evaluation point, a Gaussian process probability agent model is updated by updating the observation set, and an optimal hyper-parameter combination is obtained through multiple iterative search updates. According to the method, the evaluation frequency of hyper-parameter tuning of a machine learning model can be effectively reduced, the defect that traditional Bayesian optimization falls into the local optimal point is overcome, and hyper-parameter tuning can be more accurately and efficiently carried out.

The invention discloses an air conditioner fault diagnosis method based on a Bayesian optimization PCA-limit random tree. The air conditioner fault diagnosis method comprises the following steps: 1) acquiring operation data of an air conditioner under normal and different faults and normalizing the operation data; 2) carrying out dimensionality reduction on the normalized data through a PCA algorithm, and taking the normalized data as the input of an ExtraTree model; 3) establishing a limit random tree classification model, training and testing a classifier, and obtaining a PCA-limit random tree fault diagnosis model for an air conditioner; 4) utilizing a Bayesian optimization algorithm to optimize the feature number and the CART decision tree number of a PCA-extreme random tree fault diagnosis model after the PCA dimension reduction to obtain the optimal feature number and the optimal CART decision tree number after the dimension reduction; and 5) then, taking the calculated optimal PCA dimension-reduced feature quantity value and CART decision tree quantity value as parameters of a PCA-limit random tree model, training a sample to obtain a PCA-limit random tree fault diagnosis model, and then using the diagnosis model to diagnose real-time data.