Abstract: A multiple scenario-oriented item retrieval method and system. The method includes the steps of extracting, by Hashing learning, image features from an image training set to train a pre-built item retrieval model; when an image is in a scenario of hard samples, introducing an adaptive similarity matrix, optimizing the similarity matrix by an image transfer matrix, constructing an adaptive similarity matrix objective function in combination with an image category label; constructing a loss quantization objective function between the image and a Hash code according to the image transfer matrix; when the image is in a scenario of zero samples, introducing an asymmetric similarity matrix, constructing an objective function by taking the image category label as supervisory information in combination with equilibrium and decorrelation constraints of the Hash code; and training the item retrieval model based on the above objective function to obtain a retrieved result of a target item image.

Abstract: The present disclosure discloses an indoor structure segmentation method based on a laser measurement point cloud. The method includes the following steps: inputting an indoor three-dimensional point cloud, and performing supervoxel segmentation of the point cloud using a toward better boundary preserved supervoxel (TBBPS) segmentation method based on a given initial resolution r to extract a plane supervoxel unit; extracting surface point units in surface supervoxels by means of curvatures and normal vector changes; fitting an indoor plane model based on plane supervoxels; fitting an indoor surface model using a cylindrical simplex based on surface points; and based on an ?-expansion optimization algorithm, allocating the extracted supervoxel units and surface point units to an optimal model, so as to realize unit classification and segmentation.

Abstract: The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.

Abstract: An inorganic adhesive includes magnesium oxide, magnesium sulfate heptahydrate, silica fume, silica sol, lithium silicate, sodium silicate, citric acid, microcrystalline cellulose, cellulose nanowhiskers, amino trimethylene phosphonic acid, and water.

Abstract: The invention provides a ventilation-type solar photovoltaic self-heat-preservation outer wall system and a construction method thereof, and relates to the technical field of constructional engineering. According to the solution, the ventilation-type solar photovoltaic self-heat-preservation outer wall system comprises an inner-layer heat preservation structure and an outer-layer photovoltaic assembly, a ventilation gap is formed between the inner-layer heat preservation structure and the outer-layer photovoltaic assembly, a flow guide component is arranged in the ventilation gap, the outer-layer photovoltaic assembly is provided with a ventilation opening, and the ventilation opening is communicated with the ventilation gap. The achieved effects are good heat preservation and heat insulation performances, good ventilation and heat dissipation effects, easy mounting and dismounting, and economical and applicable performances.

Abstract: The present disclosure belongs to the technical field of image processing, and provides a multi-modal image classification system and method using an attention-based multi-interaction network. The present disclosure utilizes a U-net network structure to fuse low-level visual features and high-level semantic features. An attention network is introduced to solve the problem of weak feature discrimination, and high attention is given to discriminative features, so that the attention network plays an important role in the final classification process. A sufficient multi-modal interaction mechanism is introduced, so that more effective correlation information and discriminative information are obtained among a plurality of modalities, and sufficient interaction among the plurality of modalities is completed, thereby solving the problems of weak feature discrimination and insufficient interaction among modalities in a multi-modal image classification task.

Abstract: An online cleaning system for micro-polluted nanofiltration membranes uses forward osmosis, and a process of the online cleaning system, and relates to the field of water treatment membrane separation technique. The online cleaning system includes a nanofiltration raw water tank, a nanofiltration membrane assembly, a pure water tank, a forward osmosis feed solution tank, a forward osmosis draw solution tank, a first saline water tank, a second saline water tank and a water bath temperature control device. Some embodiments include cleaning of the nanofiltration membranes that is realized by using forward osmosis as a nanofiltration membrane cleaning system, and cyclic regeneration of the nanofiltration membranes can be realized, so that the purposes of removing dissolved organic matters in micro-polluted raw water, reducing hardness of calcium and magnesium and prolonging the service life can be achieved.

Abstract: A multiple scenario-oriented item retrieval method and system. The method includes the steps of extracting, by Hashing learning, image features from an image training set to train a pre-built item retrieval model; when an image is in a scenario of hard samples, introducing an adaptive similarity matrix, optimizing the similarity matrix by an image transfer matrix, constructing an adaptive similarity matrix objective function in combination with an image category label; constructing a loss quantization objective function between the image and a Hash code according to the image transfer matrix; when the image is in a scenario of zero samples, introducing an asymmetric similarity matrix, constructing an objective function by taking the image category label as supervisory information in combination with equilibrium and decorrelation constraints of the Hash code; and training the item retrieval model based on the above objective function to obtain a retrieved result of a target item image.

Abstract: An online cleaning system for micro-polluted nanofiltration membranes uses forward osmosis, and a process of the online cleaning system, and relates to the field of water treatment membrane separation technique. The online cleaning system includes a nanofiltration raw water tank, a nanofiltration membrane assembly, a pure water tank, a forward osmosis feed solution tank, a forward osmosis draw solution tank, a first saline water tank, a second saline water tank and a water bath temperature control device. Compared with convention techniques, some embodiments include efficient cleaning of the nanofiltration membranes that is realized by using forward osmosis as a nanofiltration membrane cleaning system, and cyclic regeneration of the nanofiltration membranes can be realized, so that the purposes of removing dissolved organic matters in micro-polluted raw water, reducing hardness of calcium and magnesium and prolonging the service life can be achieved.