Abstract: Mechanisms are provided for automatically predicting vehicle weight of a moving vehicle. Vehicle operation data is obtained from one or more on-board sensors/systems of the vehicle and features are extracted. The features are filtered based on a required working condition of the vehicle to identify intervals of features having valid feature data to thereby generate a filtered valid data. The filtered valid data is processed by a first artificial intelligence (AI) computer model based on time slices of the filtered valid data to generate a first prediction of vehicle weight, and by a second AI computer model based on buckets of key variables to generate a second prediction of vehicle weight. The first prediction is fused with the second prediction to generate a final prediction of vehicle weight which is output to downstream computing logic.

Abstract: A method, computer program, and computer system are provided for performing multiple machine learning tasks through a shared framework. Data corresponding to a plurality of predetermined machine learning tasks is received. One or more steps of the machine learning tasks associated with the received data is performed on the received data by a shared backbone of a machine learning model. The predetermined plurality of machine learning tasks is completed on the received data by a plurality of sub-networks associated with each of the plurality of predetermined machine learning tasks.

Abstract: From a set of point data, a set of scattered rays is constructed. From the set of scattered rays, a set of ray slopes is computed. The set of ray slopes is mapped to a corresponding set of trigonometric functions. Using an optimization method, a parameter of the set of trigonometric functions is selected. Using an inverse of the set trigonometric functions, a vehicle mass corresponding to the set of point data is computed. Based on the vehicle mass, a threshold braking distance of a collision avoidance system of the vehicle is adjusted, the threshold braking distance comprising a distance from an object predicted to collide with the vehicle. By braking the vehicle at least the threshold braking distance from the object, a predicted collision between the vehicle and the object is avoided.

Abstract: From a set of point data, a set of scattered rays is constructed. From the set of scattered rays, a set of ray slopes is computed. The set of ray slopes is mapped to a corresponding set of trigonometric functions. Using an optimization method, a parameter of the set of trigonometric functions is selected. Using an inverse of the set trigonometric functions, a vehicle mass corresponding to the set of point data is computed. Based on the vehicle mass, a threshold braking distance of a collision avoidance system of the vehicle is adjusted, the threshold braking distance comprising a distance from an object predicted to collide with the vehicle. By braking the vehicle at least the threshold braking distance from the object, a predicted collision between the vehicle and the object is avoided.

Abstract: The present invention may include a computer receives raw data. The computer converts the raw data into a dataset, where the dataset comprises independent variables and dependent variables. Then, the computer clusters the dataset to determine a corresponding target value to each of a plurality of clusters. The computer constructs a nonlinear programming problem based on a prior experience and generates an enlarged dataset by solving the nonlinear programming problem.

Abstract: A machine-learning system receives from multiple sensors a set of trace-data time series. Each time series contains a chronological sequence of sensor measurements of one attribute of one instance of a manufacturing product or process. The system partitions each series into a set of contiguous segments and selects one received series to be a standard series for each attribute. The starting and ending measurements of each non-standard time series are then time-aligned to the starting and ending points of the non-standard series' corresponding standard series, using a dynamic time-warping procedure. One or more segments of each aligned non-standard series are then aligned to each segment of the corresponding standard series. The resulting time-aligned, segmented time series are then incorporated into a corpus that is used by a machine-learning module to train a self-learning application.