Robert Boschgmbh

Amazon Technologies, Inc.

Shan Kang

Ruobing Chen

Rumi Ghosh

Soundararajan Srinivasan

Marc Naumann

Mahesh Goud Tandarpally

Xiao Zhang

Soundar Srinivasan

Zubin Abraham

Yuqing Xing

Anbo Chen

Techniques for forecasting long duration floating holidays in online traffic are described. According to some embodiments, a machine learning service receives a request to train a time series forecast model on time series data of a user, receives an input for the time series forecast model that comprises a first feature weight that represents a first pivot day and a second feature weight that represents a second pivot day, performs a linear interpolation on the first feature weight and the second feature weight for a day between the first pivot day and the second pivot day to generate a linearly interpolated first weight of the first feature weight for a feature vector and a linearly interpolated second weight of the second feature weight for the feature vector, determines a first coefficient for the time series forecast model based at least in part on the time series data of the user, the linearly interpolated first weight of the first feature weight from the feature vector, and the linearly interpolated second weight of the second feature weight from the feature vector, generates, by the time series forecast model comprising the first coefficient, a prediction for a future day, and transmits the prediction to the user.

Forecasting long duration floating holidays in online traffic

Methods and systems of identifying a time reduction in a manufacturing time associated with a plurality of products. One system includes an electronic processor configured to receive training data. The electronic processor is also configured to determine a first set of testing parameters from the plurality of testing parameters to remove for the assembly line based on the training data and determine a second set of testing parameters to keep by removing the first set of testing parameters from the plurality of testing parameters. The electronic processor is also configured to determine a predictive model to replace the first set of testing parameters based on the training data associated with the second set of testing parameters, and automatically update a testing process for the assembly line to turn off the first set of testing parameters and use the predictive model in place of the first set of testing parameters.

Test time reduction for manufacturing processes by substituting a test parameter

Methods and systems of identifying a time reduction in a manufacturing time associated with a plurality of products. One system includes an electronic processor configured to receive a dataset associated with an assembly line. The dataset includes a classification for each of the plurality of products produced by the assembly line, where the assembly line is associated with a plurality of tests. The electronic processor is also configured to determine a set of test combinations for the assembly line based on the plurality of tests and, for each test combination, determine a number of missing products based on the classification for each of the plurality of products. The electronic processor is also configured to determine at least one test to remove based on the number of missing products for each test combination and output a result including an indication of the at least one test to remove.

Test time reduction for manufacturing processes by removing a redundant test

A manufacturing process system comprises any number of assembly stations and test stations, a model unit, and any number of final products is provided. Any of a sample test method and the statistical distribution monitoring method performed by the model unit is configured to monitor the model quality after it is deployed and reduce potential unnecessary costs, such as warranty claim costs as a result of sending bad units to the customers, and rework costs as a result of predicting a good part as bad and wasting additional testing efforts on the bad parts. Further, both methods are configured to maximize the probability of detecting hazardous issues, while having control of the false alarm rate.

System and method for monitoring a manufacturing plant

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Mountain View, CA, United States of America

Shan Kang