Abstract: A direct current circuit breaker handcart and a direct current switch apparatus are provided. In the direct current circuit breaker handcart, the current carrying element is located on the support surface, and includes a main circuit breaker and an auxiliary circuit breaker electrically connected with each other; the commutation element is located on the support surface, is electrically connected with the current carrying element, and includes a commutation capacitor, a commutation inductor, a commutation switch assembly, and a commutation capacitor charger electrically connected with each other; the repulsive assembly is configured to control opening and breaking off of the main circuit breaker; the energy absorbing element is located on the support surface, and is electrically connected with the current carrying element and the commutation element, respectively.

Abstract: The present disclosure relates to a method and system for determining a rational width of a gob-side working face under a condition of thick and hard key strata. The method includes: constructing a piecewise function with a width of a gob-side working face as an independent variable; obtaining values of parameters of the gob-side working face; determining a solution set based on the values of the parameters of the gob-side working face, the piecewise function, and a predetermined function threshold; and determining a numerical value according to the solution set as a rational width and mining the gob-side working face according to the rational width.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MIP image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MIP image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

Abstract: A method, computer program, and computer system for detecting and classifying atrial fibrillation by receiving data corresponding to an electrocardiogram (ECG) associated with a patient, extracting RR intervals from the received ECG data, determining previous RR intervals for each of the extracted RR intervals and aggregating features associated with the RR intervals and the previous RR intervals. Patterns associated with the aggregated features are classified, and the computer may determine that a sample of the ECG data corresponding to one or more of the classified patterns contains a pattern associated with atrial fibrillation.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: A method, computer program, and computer system is provided for text error identification and correction. A text input having a phonetic component and a glyphic component is received. Information corresponding to the phonetic component and the glyphic component is coded as a fixed-length sequence. One or more candidate replacement words corresponding to the fixed-length sequence are identified. At least a portion of the text input is replaced with a candidate replacement word from among the one or more candidate replacement words.

Abstract: Provided are a method and an apparatus for receiving a query including a sequence of words. The sequence of words is converted into a sequence of feature vectors. A semantic meaning of the sequence of words is generated using a bi-directional long-short term memory (LSTM). The LSTM is regularized using a Gaussian mixed model (GMM) based on the generated semantic meaning of the sequence of feature vectors.

Abstract: A method of performing electrocardiography (ECG) analysis by at least one processor, the method including receiving ECG data that is from multiple leads; grouping the ECG data into groups of data; generating, from each group of the groups of data, a feature vector using a respective machine learning model; and performing ECG analysis using the feature vectors generated from each of the groups of data.

Abstract: A method, computer program, and computer system is provided for text error identification and correction. A text input having a phonetic component and a glyphic component is received. Information corresponding to the phonetic component and the glyphic component is coded as a fixed-length sequence. One or more candidate replacement words corresponding to the fixed-length sequence are identified. At least a portion of the text input is replaced with a candidate replacement word from among the one or more candidate replacement words.

Abstract: The present disclosure provides an electronic system with defect identification function and a method of qualifying a photoresist pattern formed using a lithography process. The electronic system includes an inspection apparatus and a processor associated with the inspection apparatus. The inspection apparatus is used for acquiring at least one image of the specimen on which a photoresist pattern is formed using a lithography process. The processor is configured to automatically apply machine learning processes implemented through one or more neural networks to identify at least one defect present in the photoresist pattern.

Abstract: A method, computer program, and computer system is provided for query matching of sentences based on co-attention scores. Two or more query inputs to a neural network are received. A correlation factor between the two or more query input is identified based on attention weights. A feature vector is generated based on the attention weights. A probability value corresponding to the two or more query inputs is determined based on the generated feature vector.

Abstract: A method and apparatus are provided that includes iteratively sampling candidates from medical records and evaluating whether ones of the candidates better explain a member from the medical records. The iterations replace the member with the candidates and depending on whether the candidates better explain the member from the medical records may be weighted in a next iteration.

Abstract: There is included an apparatus and system including an intra-heartbeat (HB) extraction code configured to extract intra-HB features from electrocardiography (ECG) signals, and an inter-HB extraction code configured to extract inter-HB features from the ECG signals, and at least one attention mechanism code configured to control at least one of the intra-HB extraction code and inter-HB extraction code based on at least one attention mechanism.

Abstract: A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MW image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MW image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

Abstract: The present disclosure provides a method for repairing defect of graphene, including: firstly introducing a composite fluid containing a reactive compound and a supercritical fluid to a reactor where the graphene powder has been placed, and impregnating the graphene powder with the composite fluid to passivate and repair the defect of graphene, wherein the reactive compound includes carbon, hydrogen, nitrogen, silicon or oxygen element; and separating the composite fluid from the graphene powder, simultaneously using molecular sieves to absorb the graphene from the composite fluid. The present disclosure further provides the graphene powder prepared by the method above. With the method of the present disclosure, it effectively reduces the ratio of the defect of the graphene, increases the content of the graphene, and has less-layer graphene with high thermal conductivity and electrical conductivity.

Abstract: A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MIP image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MIP image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

Abstract: A method of determining a risk probability of vertebral artery dissection (VAD) in a patient, including receiving medical image information of the patient and clinical report information of the patient; extracting at least one biomarker corresponding to a vertebral artery segment included in the medical image information; extracting patient history information from the clinical report information; and determining the risk probability of VAD using a deep learning classification model based on the extracted at least one biomarker and the extracted patient history information.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.