Abstract: The present application provides a method for determining a stability of a welding equipment. The method includes acquiring initial welding images of the welding equipment; obtaining at least one welding spot position of each of at least one welded workpiece in each initial welding image by processing the initial welding images; determining a welding center position of each welded workpiece based on the at least one welding spot position of each welded workpiece, and obtaining welding center positions of all welded workpieces comprised in the initial welding images; and determining a stability of welding equipment based on the welding center positions of all welded workpieces. The method determines whether the welding equipment is stable by analyzing the welding images, thereby improving an accuracy of a detection of a stability of the welding equipment.

Abstract: A method for finding crowd movements is provided. The method includes: collecting location data associated with multiple user devices, mining frequent patterns in the location data to generate multiple representative sequences, and grouping the representative sequences into clusters to find crowd movements according to sequence distances between the representative sequences. Each representative sequence includes at least one line segment between a starting location point and an end location point.

Abstract: A method for finding crowd movements is provided. The method includes: collecting location data associated with multiple user devices, mining frequent patterns in the location data to generate multiple representative sequences, and grouping the representative sequences into clusters to find crowd movements according to sequence distances between the representative sequences. Each representative sequence includes at least one line segment between a starting location point and an end location point.

Abstract: A geographical location rendering method executed in a geographical location rendering system for identifying at least one semantic region is provided. A density clustering is performed on a plurality of user generated contents of respective geographical location name information to generate a plurality of region candidates. A name extraction is performed on the region candidates to extract and confirm a common region name of the region candidates as a name of the semantic region. A region scope of the region candidates is detected as a location scope of the semantic region according to a spatial density analysis.

Abstract: A geographical location rendering method executed in a geographical location rendering system for identifying at least one semantic region is provided. A density clustering is performed on a plurality of user generated contents of respective geographical location name information to generate a plurality of region candidates. A name extraction is performed on the region candidates to extract and confirm a common region name of the region candidates as a name of the semantic region. A region scope of the region candidates is detected as a location scope of the semantic region according to a spatial density analysis.

Abstract: A method for capturing and organizing intelligence data using an organic data model includes: receiving one or more webpages containing social intelligence data; segmenting content of the one or more webpages containing social intelligence data; identifying named entities in the segmented content of the one or more webpages; identifying topics in the segmented content of the one or more webpages; identifying opinions in the segmented content of the one or more webpages; integrating the identified named entities, topics, and opinions to construct an organic object data model; and storing organic object data associated with the constructed organic object data model in an organic object database.

Abstract: A method for capturing and managing training data collected online includes: receiving a first dataset from one or more online sources; sampling the first dataset and generating a second dataset, the second dataset including the data sampled from the first dataset; receiving an annotated second dataset with predefined labels; and dividing the annotated second dataset into a training dataset and a test dataset. The disclosed method further includes: configuring a machine learning based classifier based on the training dataset; predicting at least one data point based on the training dataset and calculating a confidence score; comparing the at least one predicted data point to the test dataset; sorting the at least one predicted data point based on its confidence score; and receiving corrected training data associated with the at least one predicted data point.