Abstract: The present invention belongs to the technical field of characterization of target analyte properties, and particularly provides a mutant of a porin monomer, a protein pore comprising same, and use thereof in the detection of a target analyte, wherein an amino acid of the mutant of the porin monomer comprises a sequence set forth in SEQ ID NO: 1 or a sequence having at least 99%, 98%, 97%, 96%, 95%, 90%, 80%, 70%, 60%, or 50% identity thereto, and the amino acid of the mutant of the porin monomer comprises mutations at one or more positions corresponding to T71, S75, or F76 of SEQ ID NO: 1.

Abstract: A computer vision-based anomaly detection method and device and an electronic apparatus are disclosed. The method comprises: dividing a target picture into at least two feature regions according to different region features of the target picture, and forming training sets respectively using the feature regions corresponding to each target picture; selecting generative adversarial networks GAN as network models to be used, and training GAN network models with the training sets of different feature regions to obtain GAN network models corresponding to different feature regions; and when performing anomaly detection, performing same feature region division on a target picture to be detected, inputting different feature regions of the target picture to be detected into corresponding GAN network models to obtain a generated picture, and performing pixel value-based difference detection on the generated picture and the target picture to be detected.

Abstract: A method and device for small sample defect classification and a computing equipment are disclosed. The method comprises: separating a target to be tested into parts, and segmenting an original image of the target to be tested into at least two sub-images containing different parts according to the separated parts; establishing small sample classification models with respect to each sub-image and the original image respectively, and obtaining a classification result of each sub-image and a classification result of the original image by using corresponding classification models, wherein the classification result includes a defect category and a corresponding category probability; and determining and outputting a defect category of the target to be tested according to classification results of all the sub-images and the original image.

Abstract: A product defect detection method, device and system are disclosed.

Abstract: A method and a wearable device are provided. The method includes providing a swimming mode in the wearable device, and storing standard swimming stroke data that have been collected in advance as corresponding template data, when a monitoring process starts, activating the swimming mode according to an instruction given by a user who will immediately enter water, and after the swimming mode has been activated, controlling a sensor to collect swimming stroke data of the user; obtaining test data for identifying a swimming state of the user from the swimming stroke data; and matching the test data with each template data, when the test data successfully matches the template data, identifying the swimming state of the user to be the swimming state that corresponds to the template data.

Abstract: A computer vision-based anomaly detection method and device and an electronic apparatus are disclosed. The method comprises: dividing a target picture into at least two feature regions according to different region features of the target picture, and forming training sets respectively using the feature regions corresponding to each target picture; selecting generative adversarial networks GAN as network models to be used, and training GAN network models with the training sets of different feature regions to obtain GAN network models corresponding to different feature regions; and when performing anomaly detection, performing same feature region division on a target picture to be detected, inputting different feature regions of the target picture to be detected into corresponding GAN network models to obtain a generated picture, and performing pixel value-based difference detection on the generated picture and the target picture to be detected.

Abstract: A method and device for small sample defect classification and a computing equipment are disclosed. The method comprises: separating a target to be tested into parts, and segmenting an original image of the target to be tested into at least two sub-images containing different parts according to the separated parts; establishing small sample classification models with respect to each sub-image and the original image respectively, and obtaining a classification result of each sub-image and a classification result of the original image by using corresponding classification models, wherein the classification result includes a defect category and a corresponding category probability; and determining and outputting a defect category of the target to be tested according to classification results of all the sub-images and the original image.

Abstract: A product defect detection method, device and system are disclosed.

Abstract: The application discloses a method for monitoring a user gesture and a wearable device. The method comprises: providing an inertial sensor in a wearable device, the wearable device being located on the head of a user when being worn; monitoring movement data of the head of the user in real time, by using the inertial sensor, when the user is wearing the wearable device; conducting gesture solving by using the movement data to obtain gesture data of the head of the user; determining whether a head gesture of the user is a correct gesture according to the gesture data and a preset strategy; and sending a reminder to the user when the head gesture of the user is an incorrect gesture.

Abstract: A following method and device for an unmanned aerial vehicle and a wearable device are provided. The following method comprises: installing a plurality of receiving sensors on the unmanned aerial vehicle, wherein the plurality of receiving sensors match with one transmitting sensor in a smart control device at the user side; receiving a distance signal transmitted by the user in real time by using the receiving sensors, and calculating a relative position of the unmanned aerial vehicle with respect to the user according to the distance signal; and adjusting the horizontal position of the unmanned aerial vehicle according to the relative position, so that the relative position of the unmanned aerial vehicle with respect to the user agrees with a preset position, to realize automatic following of the unmanned aerial vehicle.

Abstract: A method and device for face tracking and a smart terminal. The method includes: collecting a frame of an image in a video stream and conducting face detection and identification, to identify a target face; determining a position of a window to be tracked that contains the target face according to the identified target face, and extracting an edge feature point in the window to be tracked of the reference frame; determining an initial position of a window to be tracked on the current frame in the video stream, and extracting an edge feature point in the window to be tracked of the current frame; calculating a relative displacement between the edge feature point of the reference frame and the edge feature point of the current frame; and adjusting the initial position of the window to be tracked of the current frame by using the relative displacement.

Abstract: A method for monitoring a swimming state by using a wearable device and a wearable device are provided. The method includes: providing a swimming mode in the wearable device, and storing standard swimming stroke data that have been collected in advance as corresponding template data, when a monitoring process starts, activating the swimming mode according to an instruction given by a user who will immediately enter water, and after the swimming mode has been activated, controlling a sensor to collect swimming stroke data of the user; obtaining test data for identifying a swimming state of the user from the swimming stroke data; and matching the test data with each template data, and when the test data that are successfully matched the template data exist, identifying the swimming state of the user to be the swimming state that corresponds to the template data associated with the test data that are successfully matched.

Abstract: The application discloses a method for monitoring a user gesture and a wearable device. The method comprises: providing an inertial sensor in a wearable device, the wearable device being located on the head of a user when being worn; monitoring movement data of the head of the user in real time, by using the inertial sensor, when the user is wearing the wearable device; conducting gesture solving by using the movement data to obtain gesture data of the head of the user; determining whether a head gesture of the user is a correct gesture according to the gesture data and a preset strategy; and sending a reminder to the user when the head gesture of the user is an incorrect gesture.

Abstract: A method and device for face tracking and a smart terminal. The method includes: collecting a frame of an image in a video stream and conducting face detection and identification, to identify a target face; determining a position of a window to be tracked that contains the target face according to the identified target face, and extracting an edge feature point in the window to be tracked of the reference frame; determining an initial position of a window to be tracked on the current frame in the video stream, and extracting an edge feature point in the window to be tracked of the current frame; calculating a relative displacement between the edge feature point of the reference frame and the edge feature point of the current frame; and adjusting the initial position of the window to be tracked of the current frame by using the relative displacement.

Abstract: A following method and device for an unmanned aerial vehicle and a wearable device are provided. The following method comprises: installing a plurality of receiving sensors on the unmanned aerial vehicle, wherein the plurality of receiving sensors match with one transmitting sensor in a smart control device at the user side; receiving a distance signal transmitted by the user in real time by using the receiving sensors, and calculating a relative position of the unmanned aerial vehicle with respect to the user according to the distance signal; and adjusting the horizontal position of the unmanned aerial vehicle according to the relative position, so that the relative position of the unmanned aerial vehicle with respect to the user agrees with a preset position, to realize automatic following of the unmanned aerial vehicle.

Abstract: A wearable device and a controlling method thereof, and a system for controlling a smart home. The wearable device comprises: a hand gesture identifying module, for by characteristic data to be identified of a wearer that is collected by a sensor, identifying out a current hand gesture action of the wearer; an appliance configuring module, for according to acquired information of each of household electrical appliances, establishing and saving correspondence relations between controlling commands of each of the household electrical appliances and corresponding hand gesture actions; and upon receiving the hand gesture action, looking up the household electrical appliance and the controlling command that match the hand gesture action by using the correspondence relations, generating a controlling message according to the matched information; a wirelessly connecting module, for receiving the controlling message and wirelessly sending the controlling message to a smart home controlling server.