The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.

An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.

According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.