Abstract: A rapid demagnetization method based on characteristics of magnetic media. In the method, basic information is obtained by a recognition module of magnetic media by means of multi-source sensing collaboration. The magnetic medium is identified by using a data processing technology and a magnetic medium identification algorithm, and then the characteristic information is extracted. Optimized set values of demagnetization parameters are obtained by a demagnetization parameter optimizing and setting module based on a demagnetization optimizing model. Demagnetization parameter set values are tracked by a closed-loop control module of a demagnetization magnetic field in combination with domain expert knowledge by using a closed-loop control mechanism integrated with a magnetic field control algorithm, a charging-discharging device, a magnetic field generating device, a magnetic field sensor and an environmental sensor, completing the rapid demagnetization of the magnetic medium.

Abstract: Disclosed is a cartridge-type rivet feeding mechanism of a flow drill screwing device including a rivet box, a magazine for storing rivets from the rivet box, a rivet pulling mechanism including a rivet pulling block, a rivet pulling block guide housing and a linear driving unit connected with the rivet pulling block, a blowing mechanism including a curved connecting tube and a third air inlet, the rivet pulling block is defined with a T-shaped through hole capable of accommodating a rivet and configured for transferring the rivet from the first connecting tube to the second connecting tube. The first air inlet and the second air inlet are both configured for introducing compressed air to push the rivet to move towards the riveter head.

Abstract: The present disclosure provides a process cartridge, a process cartridge group and an image-forming apparatus. The process cartridge includes a driving portion, configured to receive a driving force from a driving apparatus in the image-forming apparatus; an image-forming structural part, configured to be connected to the driving portion; and an information identification part, including a plurality of electrical contacts electrically connected to the image-forming apparatus. When the process cartridge is installed in the image-forming apparatus, a minimum distance between any electrical contact of the information identification part and a side plate of the image-forming apparatus adjacent to the driving portion is H, and a value of the distance H is determined corresponding to attribute information of the process cartridge and/or an installation position of the process cartridge in the image-forming apparatus.

Abstract: This disclosure features chemical entities (e.g., a compound or a pharmaceutically acceptable salt and/or hydrate and/or prodrug of the compound) that modulate (e.g., agonize) the apelin receptor (also referred to herein as the APJ receptor; gene symbol APLNR). This disclosure also features compositions containing the same as well as other methods of using and making the same. The chemical entities are useful, e.g., for treating a subject (e.g., a human) having a disease, disorder, or condition in which a decrease in APJ receptor activity (e.g., repressed or impaired APJ receptor signaling; e.g., repressed or impaired apelin-APJ receptor signaling) or downregulation of endogenous apelin contributes to the pathology and/or symptoms and/or progression of the disease, disorder, or condition.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining, by one or more processors, a target image, segmenting, by the one or more processors, a target region image from the target image, wherein the target region image comprises a target object region in the target image, analyzing, by the one or more processors, the target region image based at least in part on a machine learning model, and obtaining by the one or more processors, a recognition result based at least in part on the analysis of the target region image.

Abstract: Disclosed are a method and system for evaluating sonar self-noise at a ship design stage. The method includes: building a ship structure full-scale geometric simulation model; acquiring loss factors and sonar transducer space outfitting acoustic absorption coefficient material parameters; acquiring mechanical excitation, hydrodynamic excitation, and propeller excitation; inputting the loss factors and the sonar transducer space outfitting acoustic absorption coefficient material parameters into an established statistical energy evaluation model, and applying a mechanical excitation to a face plate of foundation of the built ship structure full-scale geometric simulation model, applying a hydrodynamic excitation to the surface of a ship hull, and applying a propeller excitation to a stern shaft to perform calculation of sonar self-noise of a ship to obtain total spectral density level of the sonar self-noise; and evaluating spectral density level calculation results by index requirements.

Abstract: Disclosed are a method and system for evaluating sonar self-noise at a ship design stage. The method includes: building a ship structure full-scale geometric simulation model; acquiring loss factors and sonar transducer space outfitting acoustic absorption coefficient material parameters; acquiring mechanical excitation, hydrodynamic excitation, and propeller excitation; inputting the loss factors and the sonar transducer space outfitting acoustic absorption coefficient material parameters into an established statistical energy evaluation model, and applying a mechanical excitation to a face plate of foundation of the built ship structure full-scale geometric simulation model, applying a hydrodynamic excitation to the surface of a ship hull, and applying a propeller excitation to a stern shaft to perform calculation of sonar self-noise of a ship to obtain total spectral density level of the sonar self-noise; and evaluating spectral density level calculation results by index requirements.

Abstract: An image recognition method includes: obtaining an image; extracting a target image region corresponding to a target part from the image, wherein the target image region includes a target object; determining a location of the target object in the target image region (i) according to pixel values of pixels in the target image region and a location relationship between the pixels, or (ii) inputting the target image region to a trained segmentation model to obtain the location of the target object in the target image region; and displaying a recognition result of the image, wherein the recognition result indicates the location of the target object in the target image region.

Abstract: Segmenting an image is disclosed including acquiring a first image and a second image, the first image and the second image being obtained based on a same imaging target, and a resolution of the first image being greater than a resolution of the second image, performing image segmentation processing based on a plurality of sub-images of the first image to obtain a first initial segmentation result, performing image segmentation processing based on the second image to obtain a second initial segmentation result, merging, based on the imaging target, the first initial segmentation result with the second initial segmentation result to obtain a target segmentation result, and outputting or storing the target segment result.

Abstract: An ultrasound diagnostic apparatus includes processing circuitry. The processing circuitry generates a first image based on an echo signal obtained by transmission and reception of ultrasound waves. The processing circuitry acquires a second image that is an image generated by a medical image diagnostic apparatus. The processing circuitry performs a registration of the first image and the second image, by discretely setting a plurality of relative positions of the first image and the second image within a specified range, calculating the similarity between the first image and the second image corresponding to the plurality of relative positions respectively, updating the plurality of the relative positions based on the calculation result of the similarity, and recalculating the similarity corresponding to the plurality of the updated relative positions respectively. The processing circuitry causes a display to display the images obtained after the registration.

Abstract: A medical image diagnostic apparatus according to an embodiment includes processing circuitry. The medical image diagnostic apparatus performs image registration between medical image data. The processing circuitry extracts a structure of the subject included in the medical image data. The processing circuitry sets a display scale of the subject to a specified value. The processing circuitry performs image registration between the medical image data on the display scale of the specified value.

Abstract: An analysis apparatus according to an embodiment includes processing circuitry. The processing circuitry performs registration between first ultrasound image data obtained at a first phase by an ultrasound diagnostic apparatus and first medical image data obtained by a medical image diagnostic apparatus other than the ultrasound diagnostic apparatus and performs registration between the first ultrasound image data and second ultrasound image data obtained at a second phase different from the first phase by the ultrasound diagnostic apparatus, to generate second medical image data registered with the second ultrasound image data; and combines the second ultrasound image data and the second medical image data to generate a single image, thereby performing registration between ultrasound image data by the ultrasound diagnostic apparatus and medical image data by the medical image diagnostic apparatus.

Abstract: An image recognition method includes: obtaining an image; extracting a target image region corresponding to a target part from the image, wherein the target image region includes a target object; determining a location of the target object in the target image region (i) according to pixel values of pixels in the target image region and a location relationship between the pixels, or (ii) inputting the target image region to a trained segmentation model to obtain the location of the target object in the target image region; and displaying a recognition result of the image, wherein the recognition result indicates the location of the target object in the target image region.

Abstract: A medical image diagnostic apparatus according to an embodiment includes processing circuitry configured to determine a plurality of small blocks for each of a plurality of pieces of medical image data, generate a plurality of superpixels corresponding to the plurality of small blocks, assign a label to at least one of the plurality of pieces of medical image data, and perform registration between the plurality of pieces of medical image data using the plurality of superpixels and the label.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining, by one or more processors, a target image, segmenting, by the one or more processors, a target region image from the target image, wherein the target region image comprises a target object region in the target image, analyzing, by the one or more processors, the target region image based at least in part on a machine learning model, and obtaining by the one or more processors, a recognition result based at least in part on the analysis of the target region image.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining, by one or more processors, a first medical image, wherein the first medical image includes a representation of a target organ, and processing, by the one or more processors, the first medical image based at least in part on a first machine learning model. A classification result associated with the first medical image and a first image of a target region in the first medical image are obtained based at least in part on the processing of the first medical image using the first machine learning model.

Abstract: Segmenting an image is disclosed including acquiring a first image and a second image, the first image and the second image being obtained based on a same imaging target, and a resolution of the first image being greater than a resolution of the second image, performing image segmentation processing based on a plurality of sub-images of the first image to obtain a first initial segmentation result, performing image segmentation processing based on the second image to obtain a second initial segmentation result, merging, based on the imaging target, the first initial segmentation result with the second initial segmentation result to obtain a target segmentation result, and outputting or storing the target segment result.

Abstract: An analysis apparatus according to an embodiment includes processing circuitry. The processing circuitry performs registration between first ultrasound image data obtained at a first phase by an ultrasound diagnostic apparatus and first medical image data obtained by a medical image diagnostic apparatus other than the ultrasound diagnostic apparatus and performs registration between the first ultrasound image data and second ultrasound image data obtained at a second phase different from the first phase by the ultrasound diagnostic apparatus, to generate second medical image data registered with the second ultrasound image data; and combines the second ultrasound image data and the second medical image data to generate a single image, thereby performing registration between ultrasound image data by the ultrasound diagnostic apparatus and medical image data by the medical image diagnostic apparatus.

Abstract: A medical image diagnostic apparatus according to an embodiment includes processing circuitry. The medical image diagnostic apparatus performs image registration between medical image data. The processing circuitry extracts a structure of the subject included in the medical image data. The processing circuitry sets a display scale of the subject to a specified value. The processing circuitry performs image registration between the medical image data on the display scale of the specified value.

Abstract: An ultrasound diagnostic apparatus includes processing circuitry. The processing circuitry generates a first image based on an echo signal obtained by transmission and reception of ultrasound waves. The processing circuitry acquires a second image that is an image generated by a medical image diagnostic apparatus. The processing circuitry performs a registration of the first image and the second image, by discretely setting a plurality of relative positions of the first image and the second image within a specified range, calculating the similarity between the first image and the second image corresponding to the plurality of relative positions respectively, updating the plurality of the relative positions based on the calculation result of the similarity, and recalculating the similarity corresponding to the plurality of the updated relative positions respectively. The processing circuitry causes a display to display the images obtained after the registration.