Abstract: The disclosure belongs to the technical field related to additive manufacturing model preprocessing, and discloses a field-based additive manufacturing digital model feature identification and extraction method and device, the method can convert the digital model represented by the facet into a signed distance field and introduce a simulated physical field of the forming/service simulation, the feature distance field after the frequency domain analysis filtering, and the geometric feature field obtained by the multi-precision convolution unit analysis according to the requirement of feature to be identified, then, multiple fields are combined to realize feature classification determination and labeling of features, and finally feature extraction is completed based on field data and isosurface and isoline reconstruction algorithms.

Abstract: Respective labels are generated automatically for a plurality of record pairs, with a label for a given pair indicating a relationship detected between the records of the pair. One or more machine learning models are trained using the labeled record pairs. The trained versions of the models are stored.

Abstract: Systems and methods are disclosed to implement a machine learned system to determine the comparative relationship between item package quantity (IPQ) information indicated in two item descriptions. In embodiments, the system employs a neural network that includes a token encoding layer, an attribute summarizing layer, and a comparison layer. The token encoding layer accepts an item description as a token sequence and encodes the tokens with token attributes that are relevant to IPQ extraction. The attribute summarizing layer uses a convolutional neural network to generate a set of fixed-size feature vectors for each encoded token sequence. All feature vectors for both item descriptions are then provided to the comparison layer to generate the IPQ comparison result. Advantageously, the disclosed neural network model can be trained to make accurate predictions about the IPQ relationship of the two item descriptions using a small set of token-level attributes as input signals.

Abstract: Respective text feature sets and non-text feature sets are generated corresponding to individual pairs of a plurality of record pairs. At least one text feature is based on whether a text token exists in both records of a pair. Perceptual hash values are used for non-text feature sets. A machine learning model is trained, using the text and non-text feature sets, to generate relationship scores for record pairs. The model includes a text sub-model and a non-text sub-model.

Abstract: A WAN-based remote mobile monitoring method and device of electrophysiological data, including microprocessor circuit, electrophysiological signal sampling processing circuit, data storage circuit, image liquid crystal circuit, real-time clock, emergent calling circuit, working power supply management circuit, wireless network interface circuit, USB interface circuit, etc, has the in-built binding IP address of the remote electrophysiological data monitoring server, TCP/IP protocol, PPP protocol and BlueTooth protocol. Application controls self-adaptive analysis computing, exception event warning and alarming privilege graded setting, event data package combination, network digital communication, data storage circuit area management, safety information data storage management and remote emergent calling of the remote mobile monitoring devices.