Abstract: A sealing device for positioning points in holes of process grouting is provided, which includes an isolation component provided on an anchor cable body. The isolation component is detachably connected to an isolation sleeve through a coupling component, the isolation sleeve can be provided on the anchor cable body to send the isolation component into a corresponding position in a borehole and cooperates with the isolation component to perform a fixed-point isolation on a surrounding rock area in the borehole. A combination of the isolation component and the isolation sleeve sleeved on the anchor cable body is used, and the inflation and expansion of the first and second airbags are used to achieve tight contact with a hole wall, thereby forming effective isolation at specific locations within the borehole, thereby laying a foundation for dynamic grouting in stages and regions. The docking part is provided with an installation groove.

Abstract: A battery production device, a battery production apparatus, and a battery production method are disclosed. The battery production device includes a frame, a moving mechanism, and a cover body. The moving mechanism is connected to the frame and has a first station, the moving mechanism being configured to move a first support cup to the first station, the first support cup including a first mounting cavity adapted to receive a battery. The cover body is configured to seal the first support cup located at the first station, thereby sealing the first mounting cavity.

Abstract: Control device controls loading manipulator to transfer battery cell to main detection chamber and open first vacuum valve, evacuates the main detection chamber through first vacuum pump, closes the first vacuum valve after completing evacuation, and performs major leak detection on the battery cell through negative pressure gauge; if passed, controls second vacuum valve to open, to start second vacuum pump to evacuate the battery cell, and after the evacuation, controls first helium injection valve of helium pipeline to open, to inject helium into the battery cell, and closes the first helium injection valve after the helium injection is completed, and performs helium detection on the battery cell through helium detector; if major leak detection is failed, opens vacuum breaking valve, and breaks vacuum of the battery cell and the main detection chamber through atmospheric pipeline; and controls unloading manipulator to transfer the battery cell to re-detection chamber for re-detection.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

Abstract: A method for tracking multiple targets includes identifying a primary target from a plurality of targets based on a plurality of images obtained from an imaging device carried by a movable object via a carrier, determining a target group including one or more targets from the plurality of targets, where the primary target is always in the target group. Determining the target group includes determining one or more remaining targets in the target group based on a spatial relationship or a relative distance between the primary target and each target of the plurality of targets other than the primary target. The method further includes controlling at least one of the movable object or the carrier to track the target group as a whole.

Abstract: A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

Abstract: A computer-implemented method for tracking multiple targets includes identifying a primary target from a plurality of targets based on a plurality of images obtained from an imaging device carried by an aerial vehicle via a carrier, determining a target group including one or more targets from the plurality of targets, where the primary target is always in the target group. Determining the target group includes determining one or more remaining targets in the target group based on a spatial relationship or a relative distance between the primary target and each target of the plurality of targets other than the primary target. The method further includes controlling at least one of the aerial vehicle or the carrier to track the target group as a whole.

Abstract: A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts.

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: A computer-implemented method for tracking multiple targets includes identifying a primary target from a plurality of targets based on a plurality of images obtained from an imaging device carried by an aerial vehicle via a carrier, determining a target group including one or more targets from the plurality of targets, where the primary target is always in the target group. Determining the target group includes determining one or more remaining targets in the target group based on a spatial relationship or a relative distance between the primary target and each target of the plurality of targets other than the primary target. The method further includes controlling at least one of the aerial vehicle or the carrier to track the target group as a whole.

Abstract: A computer-implemented method for tracking multiple targets includes identifying a plurality of targets based on a plurality of images obtained from an imaging device carried by an unmanned aerial vehicle (UAV) via a carrier, determining a target group comprising one or more targets from the plurality of targets, and controlling at least one of the UAV or the carrier to track the target group.

Abstract: A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

Abstract: A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.