Sap Se

Nanyang Technological University

Chen Wang

Peng Ni

Yew Soon Ong

Lihua Xie

Junsong Yuan

Bingshui Da

Abhishek Gupta

Methods, systems, and computer-readable storage media for receiving a dataset, the dataset including a plurality of data values, clustering data values of the plurality of data values into a plurality of input feature clusters in input feature space, training a local multi-task Gaussian process (MTGP) for each input feature cluster to provide optimized hyper-parameters in hyper-parameter space, an optimized hyper-parameter being provided for each input feature cluster, merging data values based on the optimized hyper-parameters, and distances between hyper-parameter clusters in the hyper-parameter space to provide a plurality of merged data values, and providing a LL-MTGP model based on the merged data values.

Exploiting local inter-task relationships in adaptive multi-task learning

A simultaneous localization and mapping method and corresponding system are disclosed. The method comprises: receiving a frame of three dimensional point cloud data from a three dimensional point cloud sensor coupled to a robot; receiving inertial measurement data indicative of a change in pose of the three dimensional point cloud sensor; using the inertial measurement data to estimate a rotation between the received frame of three dimensional point cloud data and a key frame of three dimensional point cloud data; applying a rotation to the received frame of three dimensional point cloud data to obtain an aligned frame of three dimensional point cloud data, the aligned frame of three dimensional point cloud data having an orientation aligned with the key frame of three dimensional point cloud data; and estimating a translation of the aligned frame of three dimensional point cloud data from the key frame of three dimensional point cloud data.

Simultaneous localization and mapping methods and apparatus

A proposed solution to analyze event data from sensors of devices of a manufacturing environment to identify relationships of events to predict faults in devices is disclosed. The analysis includes conditional probability model and Apriori model. The relationships are used to determine a device health index which is compared to real-time event data to predict faults in a device.

Fault detection using event-based predictive models

A technology for path determination using robust optimization is provided. In accordance with one aspect, a network graph of a network is generated. The network graph comprises nodes corresponding to points in the network, and edges which connect the nodes. Costs for each edge of the network are determined and modeled using reference point, upper bound and lower bound parameters. A user input which includes a source node, destination node, and cost target may be received from a client device. A resultant path connecting the source node and destination node are determined by solving a target-oriented robust optimization problem, which optimizes a cost of the resultant path based on the modeled costs of the edges. The resultant path is displayed on a user interface of the client device.

Path determination using robust optimization

Methods, systems, and computer-readable storage media for receiving data representative of the temporal graph, the data representing vertices, edges between vertices, and temporal features, determining a set of earliest-arrival dependencies, each earliest arrival dependency including an earliest feasible edge between vertices from a list of edges of the temporal graph, providing data representative of an edge-scan-dependency graph (ESD-graph) based on the data representative of the temporal graph, and the set of earliest-arrival dependencies, the ESD-graph including vertices representing edges of the temporal graph, and edges representing earliest-arrival dependencies between vertices, providing data representative of a level-assigned ESD-graph including a level assigned to each vertex of the ESD-graph, and determining earliest-arrival times between a source vertex, and each vertex of the temporal graph by executing a parallel edge scan of the level-assigned ESD-graph.

Growing community of inventors

Singapore, Singapore

Chen Wang