Abstract: The present disclosure relates to a gas purification apparatus and a trace substance detection device. The gas purification apparatus includes a first purification component, a second purification component and a switching component, wherein the switching component can be switched between a first state and a second state, the first purification component and a component to be purified form a gas purification loop in the first state, and the second purification component can provide a regeneration gas for the first purification component in the second state, so that water vapor and impurities in the first purification component are discharged to outside.

Abstract: The present application discloses a cross-media retrieval method based on deep semantic space, which includes a feature generation stage and a semantic space learning stage. In the feature generation stage, a CNN visual feature vector and an LSTM language description vector of an image are generated by simulating a perception process of a person for the image; and topic information about a text is explored by using an LDA topic model, thus extracting an LDA text topic vector. In the semantic space learning phase, a training set image is trained to obtain a four-layer Multi-Sensory Fusion Deep Neural Network, and a training set text is trained to obtain a three-layer text semantic network, respectively. Finally, a test image and a text are respectively mapped to an isomorphic semantic space by using two networks, so as to realize cross-media retrieval. The disclosed method can significantly improve the performance of cross-media retrieval.

Abstract: Disclosed is a boundary filtering method for intra prediction, relating to the video encoding technology filed. Whether boundary filtering is performed on an intra prediction block or not is adaptively selected by means of a rate distortion optimization decision; during filtering, a filter coefficient exponentially attenuated relative to distance to boundary is adopted to perform filtering on the first N rows or the first N columns of the intra prediction block by means of an intra prediction block filter, and different filtering strengths are used according to different sizes of the prediction blocks. Therefore, the boundary distortion problem of intra prediction block is solved, the intra prediction precision is improved, and the encoding efficiency of intra prediction block is increased; and the practicability and the robustness of the boundary filtering technology are improved.

Abstract: A video action detection method based on a convolutional neural network (CNN) is disclosed in the field of computer vision recognition technologies. A temporal-spatial pyramid pooling layer is added to a network structure, which eliminates limitations on input by a network, speeds up training and detection, and improves performance of video action classification and time location. The disclosed convolutional neural network includes a convolutional layer, a common pooling layer, a temporal-spatial pyramid pooling layer and a full connection layer. The outputs of the convolutional neural network include a category classification output layer and a time localization calculation result output layer. The disclosed method does not require down-sampling to obtain video clips of different durations, but instead utilizes direct input of the whole video at once, improving efficiency.

Abstract: A method and a device for MCMC framework-based sub-hypergraph matching are provided. Matching of object features is performed by constructing sub-hypergraphs. In a large number of actual images and videos, objects vary constantly, and contain various noise points as well as other interference factors, which makes image object matching and searching very difficult. Perform object feature matching by representing the appearance and positions of objects by sub-hypergraphs allows for faster and more accurate image matching. Furthermore, a sub-hypergraph has several advantages over a graph or a hypergraph: on one hand, a sub-hypergraph has more geometric information (e.g. angle transformation, rotation, scale, etc.) than a graph, and has a lower degree of difficulty and better extensibility than a hypergraph. On the other hand, the disclosed method and device have stronger capabilities to resist interference and good robustness, and are adaptable to more complex settings, especially with outliers.

Abstract: There are provided an ion mobility spectrometer and a sniffer. The ion mobility spectrometer includes: an ion migration tube; a sampling gas path having a sampling device configured to temporarily store a sample gas collected by a sampling head in a sampling pipe; a sample introduction gas path having two ends in communication with the gas inlet and outlet of the ion migration tube respectively, and configured to introduce a carrier gas within the ion migration tube into the sampling pipe and to carry a sample gas temporarily stored in the sampling pipe into the ion migration tube; and a valve assembly configured to only allow gas to flow from the sampling device to the sampling pipe in a sampling state, and to only allow gas to flow from the ion migration tube through the sampling pipe back to the ion migration tube in a sample introduction state.

Abstract: The present disclosure provides a trace detection device. The trace detection device includes: a box body comprising a main body frame and a top plate, the top plate and the main body frame forming a fully enclosed cavity; an ion migration tube assembly in the cavity and on a first side of the cavity; and a preamplifier and high voltage circuit board in the cavity and on a second side of the cavity, the second side being opposite to the first side.

Abstract: A memory network method based on automatic addressing and recursive information integration. The method is based on a memory neural network framework integrating automatic addressing and recursive information, and is an efficient and lightweight memory network method. A memory is read and written by means of an automatic addressing operation having low time and space complexity, and memory information is effectively utilized by a novel computing unit. The whole framework has the characteristics of high efficiency, high speed and high universality. The method is suitable for various time sequence processing tasks, and shows the performance superior to that of the conventional LSTM and the previous memory network.

Abstract: The present disclosure discloses a gas chromatograph-ion mobility spectrometry combined equipment, including: a gas chromatograph device for pre-separating a to-be-detected sample to form a pre-separated sample; an ion mobility spectrometry device in fluid communication with an outlet of the gas chromatograph device for detecting the pre-separated sample; and a gas circulating device in fluid communication with a discharged gas interface of the ion mobility spectrometry device for processing a discharged gas from the ion mobility spectrometry device, wherein the gas circulating device is further in gas communication with the gas chromatograph device for conveying a part of the discharged gas to the gas chromatograph device to serve as a carrier gas, which drives the to-be-detected sample to enter the gas chromatograph device. The miniaturization and the portability of the gas chromatograph-ion mobility spectrometry combined equipment are improved.

Abstract: Provided is an a priori constraint and outlier suppression based image deblurring method. A convolution model is used for fitting a blurring process of a clear image and then the blurred image I is restored, so that the purpose of image deblurring is achieved. The method comprises an evaluation process of the significant structure of a blurred image, a process of blurring kernel estimation and outlier suppression, and a process of restoring the blurred image by non-blind deconvolution. A structure in the blurred image is obtained by use of L0 norm constraint and heavy-tailed a priori information. The L0 norm constraint is used to evaluate the blurring kernel. The evaluated blurring kernel is subjected to outlier suppression. The final restored image is obtained by using a non-blind deconvolution algorithm. The present invention can prominently improve the restoration level of the blurred image.

Abstract: An image feature extraction method for person re-identification includes performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction; performing the aligned local descriptor extraction by processing an original image by affine transformation and performing a summation pooling operation on image block features of same regions to obtain an aligned local descriptor; reserving spatial information between inner blocks of the image for the aligned local descriptor; and performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature. The method can resolve the problem of feature misalignment caused by posture changes of pedestrian, etc., and eliminate the effect of unrelated backgrounds on re-recognition, thus improving the precision and robustness of person re-identification.

Abstract: A back-propagation significance detection method based on depth map mining, comprising: for an input image Io, at a preprocessing phase, obtaining a depth image Id and an image Cb with four background corners removed of the image Io; at a first processing phase, carrying out positioning detection on a significant region of the image by means of the obtained image Cb with four background corners removed and the obtained depth image Id to obtain the preliminary detection result S1 of a significant object in the image; then carrying out depth mining on a plurality of processing phases of the depth image Id to obtain corresponding significance detection results; and then optimizing the significance detection result mined in each processing phase by means of a back-propagation mechanism to obtain a final significance detection result map. The method can improve the detection accuracy of the significance object.

Abstract: Provided by the present invention are point cloud encoding and decoding methods, an encoding device and a decoding device. The method comprises: determining a point set composed of K nearest neighbor points of a current point; determining a point set composed of L next nearest neighbor points of the current point; determining a preferred nearest neighbor point set of the current point according to the point set composed of K nearest neighbor points of the current point and the point set composed of L next nearest neighbor points of the current point; and performing encoding according to the preferred nearest neighbor point set of the current point. Therefore, point cloud encoding performance may be improved by means of selecting a neighbor point set having an appropriate distribution.

Abstract: Disclosed in the present invention is a point cloud attribution compression method based on deleting 0 elements in a quantisation matrix, including optimizing a traversal sequence for a quantisation matrix and deleting the 0 elements at the end of the data stream. The present invention may use seven types of traversal sequences at the encoding end of the point cloud attribute compression, such that the distribution of the 0 elements in the data stream may be more concentrated at the end thereof. The 0 elements at the end of the data stream may be deleted, removing redundant information and reducing the quantity of data to be entropy encoded. At the decoding end, the point cloud geometric information may be incorporated to supplement the deleted 0 elements and the quantisation matrix may be restored according to the traversal sequence, thereby improving compression performance without introducing new errors.

Abstract: There are provided an ion mobility spectrometer and a sniffer. The ion mobility spectrometer includes: an ion migration tube; a sampling gas path having a sampling device configured to temporarily store a sample gas collected by a sampling head in a sampling pipe; a sample introduction gas path having two ends in communication with the gas inlet and outlet of the ion migration tube respectively, and configured to introduce a carrier gas within the ion migration tube into the sampling pipe and to carry a sample gas temporarily stored in the sampling pipe into the ion migration tube; and a valve assembly configured to only allow gas to flow from the sampling device to the sampling pipe in a sampling state, and to only allow gas to flow from the ion migration tube through the sampling pipe back to the ion migration tube in a sample introduction state.

Abstract: Embodiments of the present disclosure provide a gas purifying device and an ion migration spectrometer. The gas purifying device includes a first purificant vessel, a second purificant vessel and a valve communicated between the first purificant vessel and the second purificant vessel. The valve is configured to allow a gas flows from the second purificant vessel to the first purificant vessel in a first state and to permit the gas to flow from the first purificant vessel to the second purificant vessel in a second state.

Abstract: The present invention relates to intelligent oil sludge treatment apparatuses and treatment processes. The treatment apparatus includes an integrative device, an oil removal device, a separation device, a sludge collection tank, a dewatering device, a pyrolysis device, an agent tank, a deodorization tower, a crude oil tank, a light oil tank, a separator, a condenser, a desulfurization tower, a clean water tank, a sewage station, and a steam boiler, where an outlet of the integrative device is connected to an inlet of the oil removal device; the oil removal device is configured to remove crude oil from oil slurry; the oil removal machine collects the crude oil to the crude oil tank, discharges stench into the deodorization tower, and discharges the slurry into the separation device; and the separation device is configured to perform a solid-liquid separation operation.

Abstract: The present invention relates to the biomedicine field, in particular to an anti-human PD-L1 humanized monoclonal antibody and its applications. The invention obtains an anti-human PD-L1 humanized monoclonal antibody with good specificity, high affinity and stability by screening, and the antibody can specifically bind to human PD-L1 instead of binding to members of B7 family, and it can bind to active T-cells to strengthen the activation of T-cells, so it can significantly inhibit the growth of tumor.

Abstract: An attribute-based point cloud strip division method. The method comprises: first, performing spatial division of a certain depth on a point cloud to obtain a plurality of local point clouds; and then, sorting the attribute values in the local point clouds, and on the basis of such, further performing point cloud strip division so as to obtain point cloud strips that have low geometric overhead and a uniform number of points. By means of comprehensively using the spatial position and attribute information of the point clouds, the points having similar attributes and related spatial positions are gathered as much as possible in one strip during strip division, which is convenient for making full use of the redundancy of the attribute information between adjacent points, and improving the performance of point cloud attribute compression.

Abstract: An enhanced graph transformation-based point cloud attribute compression method. For point cloud attribute information, a point cloud is first subjected to airspace division by using a K-dimension (KD) tree; a new graph transformation processing method in combination with spectral analysis is provided; the point cloud is then subjected to spectral clustering on graphs in coded blocks of the point cloud; expansion is performed on the basis of existing graph transformation to implement a local graph transformation scheme; enhanced graph transformation with two transformation modes is formed; the compression performance of graph transformation is improved. The method comprises: performing color space transformation of point cloud attributes; dividing the point cloud by using the KD tree to obtain the coded blocks; performing spectral clustering-based enhanced graph transformation; performing transformation mode decision; and performing uniform quantization and entropy coding.