38 results about "Model tuning" patented technology

Database mining, analysis and optimization techniques in conjunction with the model-based functional coverage analysis are used to turn raw verification and coverage data into design intelligence (DI) and verification intelligence (VI). The required data and attributes are automatically extracted from verification, simulation and coverage analysis databases. Design finite state machine extraction, design functional event extraction, and automatic coverage model generation and optimization techniques are applied to the design HDL description. Coverage model tuning and optimization directives, as well as test spec tuning and optimization directives are generated based on the analysis and mining of various verification, simulation, and coverage databases. An integrated web-based interface portlet is used for access, analysis and management of the resulting databases, generated reports and verification directives. Dissemination rules are used to automatically generate and distribute analysis reports and verification directives to engineers at wired or wireless interface devices via Internet or Intranet.

Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.

A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

A methodology and system for performing radio network coverage surveys of multiple site candidates concurrently using one reserved channel based on reverse path signal strength measurements of a signal from a test mobile station transmitter (100) to a plurality of prospective base station receiver sites (200, 200′, . . . ) over a geographical area is presented. The instantaneous position, time-stamp and power of the Transmitter (101) is recorded and stored as it moves along a test route(s). Stationary Test Receiver(s) (201) tuned to the selected channel at each prospective site location (200, 200′, . . . ) save received instantaneous signal strength and, it's time-stamp so they can matched up to the position of the Test Transmitter (101) and used to ascertain received signal strength at each site. Results can be useful for ascertaining optimum site locations, antenna configurations, propagation model tuning, frequency planning, pilot planning and extraction of signal correlation statistics.

The invention discloses an intelligent image recognition system for big data collection and analysis and an application method thereof, and relates to the technical field of intelligent data analysis. The system of the present invention includes an intelligent cloud server; the intelligent cloud server includes a computing server and a storage server, and the computing server is equipped with a data reading module, a video stream data processing module, an AI image recognition module, a data storage module, and a model tuning module. In the image recognition system, the storage server is provided with an interactively connected video stream storage database, a video stream management module, and a data center database. The invention restores consumers' non-private real digital behaviors and produces more commercial value. In this process, there are no problems of delay, omission, slow speed, large errors, and high costs, and solves the bottleneck of consumers' real-time digital behavior analysis. Make business analysis closer to reality, bring more valuable analysis results to the brand side, and guide the brand to optimize the consumption path globally.

The invention discloses a method for tuning and managing a deep learning model, which includes: in response to the number of pictures in a data set that needs to be predicted is greater than a set number threshold, the data set is divided into several sub-data sets on average, and each sub-data set is processed by a deep learning model. The data set is predicted to obtain the first prediction data; the judgment data of each sub-data set is obtained through manual prediction and judgment; the Kalman filter is performed on the first prediction data and judgment data of each sub-data set to obtain the first prediction data of each sub-data set Two prediction data; obtain the error rate of the deep learning model according to the first prediction data and the second prediction data; trigger the retraining of the deep learning model in response to the error rate exceeding the error rate threshold. The invention also discloses a device, equipment and medium. The principle of the invention is easy to understand, easy to operate and realize, and the accuracy is guaranteed, and the function of automatic model retraining and tuning is optimized.

The fault warning method of the pitch motor temperature sensor of wind turbine based on the LightGBM algorithm includes the following steps: data collection and processing, collecting enough fan operation data before modeling, and performing preliminary processing; training the model, selecting a sensor with a pitch motor Perform model training on the faulty data and save it as a lightGBM model; parameter adjustment optimization, in order to make the algorithm achieve the optimal training effect, adjust the parameter variables of the model; run the model to get the early warning result, after the model is saved, in the new data prediction process , you need to load the saved model first, the data that needs to be predicted, judge the fault point by comparing the predicted value with the actual value, and output the early warning information; then analyze the early warning information, find the root cause of the failure, and give reasonable maintenance suggestions . The invention overcomes the deficiencies of the prior art, utilizes the distributed and high-efficiency characteristics of the LightGBM, and well solves the problems of difficult early warning modeling and low accuracy of pitch motor faults of wind turbines.

Disclosed are a model updating method, device and equipment. Through the solution provided by the embodiment of this specification, given the shadow set S and the target sample, the prior probability α of the training sample included in the shadow set is calculated respectively, and when sampling and training the model according to the shadow set S, the The sampling training parameter distribution of the obtained sampling model, and then calculate the posterior probability P of the target sample z in the shadow set according to the training parameter distribution of the given model and the eigenvalues ​​of the target sample, so that the posterior probability P and the prior The difference of the probability α is used to evaluate whether the trained model is qualified for the degree of privacy leakage of the training sample set. Make model adjustments to avoid privacy data leakage.