317results about How to "Improve fusion efficiency" patented technology

Disclosed is a system for extracting energy from controlled fusion reactions. The system includes a central target chamber for receiving fusion target material. A plurality of energy drivers are arranged around the target chamber so as to supply energy to fusion target material in the chamber to initiate a controlled fusion reaction of the materials releasing energy in the forms of fusion plasma and heat. A plurality of structures for extracting energy from the fusion reaction are provided, and comprise devices to extract high voltage DC power from the fusion plasma; and means to extract thermal energy from the central target chamber. Power to the energy drivers may be supplied from high voltage DC power extracted from the fusion reactions. The energy drivers may use an Apodizing filter to impart a desired shape to the wavefront of the driving energy for causing the fusion reactions, to avoid hydrodynamic instabilities.

The invention discloses a pixel-level alignment algorithm for color images to depth images of a color depth camera. The pixel-level alignment algorithm comprises the steps of 1) determining an offset between an infrared image and a depth image, 2) calibrating the internal parameters of an infrared camera and a color camera, respectively, 3) calibrating relative postures between the infrared camera and the color camera, 4) calibrating the parameters of a depth camera model, 5) starting from one pixel point of the color image, finding out corresponding pixel points on the depth image by use of the internal and external parameters of an infrared image and a color image, thereby determining the depth of the pixel points of the color image. According to the pixel-level alignment algorithm, the corresponding depth information is directly obtained from the color image, the alignment of the color information to the depth information can be realized, a fusion image of a larger size can be obtained, and the information is richer.

The invention provides a cell electrofusion chip device based on a columnar microelectrode array, which consists of a columnar microelectrode array chip, a printed circuit board and a flow passage control module. The columnar microelectrode array chip consists of a quartz substrate layer, a metal lead layer, a polymer insulated layer and a columnar microelectrode layer sequentially from bottom to top, wherein the metal lead layers of the columnar microelectrode array chip is electrically connected with the periphery printed circuit board in a bonding way, leads outside electrical signals onto a columnar microelectrode, and leads a gradient electric field with enough strength to be formed between adjacent columnar microelectrodes; and the flow passage control module covers the columnar microelectrode array chip. In the invention, by loading electrical signals on the columnar microelectrode, queue and fusion among cells are controlled; simultaneously, the superiority that the columnar microelectrode array benefits the flow of the cells in chamber-tracts is utilized for realizing continuous stream fusion of the cells; and with the combination of the design of the array columnar microelectrode, the continuous, high-efficient and high-throughput fusion of the cells in the device is realized.

The embodiment of the invention provides image fusion equipment and an image fusion method. The image fusion equipment comprises a light acquisition device, an image processor and an image sensor witha red, green and blue RGB channel and an infrared IR channel; the light acquisition device blocks the spectrum of a first predetermined wavelength interval in incident light to obtain target light; the first predetermined wavelength interval is that a response difference of the RGB channel and the IR channel in the image sensor on an infrared band is higher than a wavelength range of the spectrumof a first predetermined threshold; the image sensor converts the target light into an image signal through the RGB channel and the IR channel; and the image processor parses the image signal into acolor signal and a luminance signal that is sensitive to the infrared band, and fuses the color signal and the luminance signal to obtain a fused image. According to the scheme of the invention, the operation of removing infrared components from the color signal by the image processor can be simplified, and the fusion efficiency can be improved.

The invention discloses a hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE, which solves the problems of serious spectral distortion and insufficient spatial detail injection in the existing hyperspectral image fusion, and comprises the following implementation steps of: obtaining an image data set for preprocessing; constructing and training a hyperspectral image spectrum constraint adversarial auto-encoder network; extracting spatial features of the hyperspectral image; acquiring space details of the enhanced panchromatic image; fusing the spatial information of the hyperspectral image and the enhanced panchromatic image; constructing a gain matrix; and obtaining a hyperspectral image with high spatial resolution. Hyperspectral and panchromatic image space information is considered at the same time, deep space features are extracted through AAE, spectral loss is effectively reduced, and the space resolution is effectively improved. Simulation proves that the hyperspectral image fusion method is better in fusion index and visual effect space and spectral performance, better in space detail maintenance and more perfect in spectral information, and is used for hyperspectral image fusion processing.

The invention provides an infrared and visible light image fusion method based on salient objects. The infrared and visible light image fusion method based on the salient objects includes following steps: building nonlinear scale space representation for an infrared image and a visible light image which respectively comprise a plurality of objects in a given scene; using a visual attention computational model to compute visual attention salient maps of the infrared image and the visible light image based on the nonlinear scale space representation of the images; using a return inhibition mechanism to select salient object areas from the infrared image and the visible light image based on the visual attention salient maps of the infrared image and the visible light image, and computing all salient object areas in the whole scene; performing rectification operation on the infrared image and the visible light image, using a pixel level fusion algorithm to perform fusion treatment on the salient object areas, and using a feature level fusion algorithm to perform fusion treatment on non-salient object areas; generating a fusion image of the infrared image and the visible light image of the whole scene by synthesizing results.

Disclosed is an antibody against a secreted form of GPC3 capable of detecting a secreted form of glypican 3 (GPC3) in a test sample. It is possible to determine whether a subject suffers from cancer, in particular hepatoma. Also disclosed is an antibody against GPC as well as a cell disrupting agent and an anti-cancer agent comprising the same, which can disrupt cells, in particular cancer cells.

The invention discloses a video event recognition method based on a top-down motion attention mechanism, which comprises the following steps of: 1, detecting points of interest of each frame in each video in a video set on a computer by using Gaussian difference detector, wherein the video set comprises a training video set and a testing video set; 2, extracting scale-invariant characteristic description sub-characteristics and light stream characteristics from the detected points of interest of each frame; 3, establishing an apparent word list and a motion word list; 4, learning the probability of each motion word about each type of events on the training video set and establishing a motion information-based attention histogram; 5, calculating the similarity between videos in the video set by using the distance of a bulldozer, and generating a kernel function matrix; and 6, training a support vector machine classifier by using the obtained kernel function matrix so as to obtain classifier parameters, classifying the tested video sets, and outputting classification results.

The invention claims a DSRC-based (dedicated short-range communications based) intelligent vehicle map fusion system and method; the system comprises a vehicle positioning module, a local map generating module, a DSRC module, an ADAS (advanced driver assistance systems) map interface, a target vehicle screening module and a map fusion module; the vehicle positioning module is used for acquiring position and attitude of a main vehicle, the local map generating module is used for generating a local map corresponding to the current position of the main vehicle according to data of a vehicular sensor, the DSRC module is used for receiving position, attitude and local map information issued by a target vehicle within a communication range, and transmitting the position, attitude and local mpa of the main vehicle itself, the ADAS map interface module is used for providing protocols and interfaces to access an ADAS map, the target vehicle screening module is used for screening a target vehicle on the same lane as the main vehicle, and the map fusion module is used for fusing the local map of the main vehicle and that of the screened target vehicle to generate an extended local map of the main vehicle. The DSRC-based intelligent vehicle map fusion system and method can provide multi-vehicle sensing capacity sharing to extend the ambient sensing range, helping improve intelligent vehicle assisted driving/automatic driving performance.

The invention discloses a system fusing Internet of Things and telecommunication network. The system comprises a household integrated gateway and an Internet of Things application platform server, wherein the household integrated gateway is used for providing network access service for a mobile telecommunication network terminal and a WSN (Wireless Sensor Network) terminal, transmitting WSN control information and WSN data information to the Internet of Things application platform server, and forwarding the WSN control information sent by the Internet of Things application platform server to the WSN terminal; and the Internet of Things application platform server is used for sending the WSN control information according to the received WSN control request. The invention realizes the fusion of the Internet of Things and the telecommunication network on the basis of the household integrated gateway.

Provided is a method for diagnosing cancer by detecting a novel cancer marker. Cancer can be diagnosed by detecting soluble glypican 3 in a test sample.

The invention discloses a seismic multi-attribute RGBA (red, green, blue, alpha) color fusion method which comprises the following steps of: (a) firstly, respectively transforming three different input seismic attribute values (Vi=1, 2, 3) into three red (IR), green (IG) and blue (IB) components by a certain transformation T; (b) calculating a color IRGB of a point by utilizing a color mixing rule of an RGB color space; and (c) finally, obtaining an RGBA fused image and if a formula that x=(x, y, z) is met, popularizing to the three-dimensional data cube condition to generate an RGBA color fused data cube. The seismic multi-attribute RGBA color fusion method has simple steps and high fusion efficiency; and cost of seismic multi-attribute RGBA color fusion is greatly reduced.

A vertebral fixation plate assembly for insertion into the resection site between two vertebrae of a patients vertebral column and fixation to front or lateral side of the two adjacent vertebral bodies above and below the resection site by means of surgical implantation is disclosed to include a fixation plate that has a plurality of round holes and elongated holes and an opening on the middle, a plurality of bone screws respectively inserted through the round holes and elongated holes for fastening the fixation plate to the patient's vertebral column, and a cage affixed to the opening on the fixation plate and holding holes cut through the periphery for bone fusion and grow. The cage is linked to a vertebral spacer for artificial vertebrae by means of bone fusion to prevent vertebral sinking or slipping of the vertebral spacer and to improve bone fusion efficiency.

The invention discloses a method for tracking mixing features. Feature points of key frame images are extracted and saved by SIFT (scale-invariant feature transform) algorithm. A KLT (Kanade-Lucas-Tomasi feature tracker) algorithm thread tracks the feature points of the key frame images by KLT algorithm to obtain the feature points of current frame images, detects whether number of the feature points is sufficient or not for the next tracking after homography is successfully calculated, and notifies a SIFT algorithm thread when the feature points are insufficient. The SIFT algorithm thread extracts and matches the feature points according to the notice given by the KLT algorithm thread, and returns the matched feature points to the KLT algorithm thread. The KLT algorithm thread blends the received feature points returned by the SIFT algorithm thread into tracking feature points remained in the KLT algorithm thread for tracking. The invention further discloses a device for tracking mixing features. By the method and the device, stable tracking is achieved. The method and the device are applicable to camera tracking fields such as virtual studios and virtual sports.

The embodiment of the invention discloses a game data processing method and device and a storage medium, and the method comprises the steps: obtaining a virtual scene rendering request sent by a firstgame user through a first client when a cloud server builds a long connection with a target cloud game; obtaining a game identifier of the target cloud game and a scene identifier of the virtual scene from the virtual scene rendering request, and querying a cache matching scene matched with the game identifier and the scene identifier based on a scene rendering rule to obtain virtual rendering data corresponding to the virtual scene; returning the virtual rendering data to the first client, thereby enabling the first client to perform data fusion on the reality acquisition data and the virtual rendering data based on the depth value associated with the virtual scene when the first client acquires the reality acquisition data in the reality environment where the first game user is located,and obtaining a fused data image. By adopting the method and the device, the rendering time delay can be reduced, and the data fusion efficiency and quality can be improved in an augmented reality scene.

Provided is a cell growth inhibitor that can be used for treating diseases based on abnormal cell proliferation, and in particular cancer. The cell growth inhibitor contains an anti-glypican 3 antibody as an active ingredient.

The present invention relates to a resonant vacuum arc discharge apparatus for producing nuclear fusion. A resonant high-frequency high-voltage alternating current (AC) power supply is used to efficiently power a fusion tube normally containing deuterium, tritium and/or helium-3 vapor. Metals that can hold large amounts of hydrogen isotopes such as palladium and titanium can be used to increase the target density. The nuclear fusion device can be used for energy production, well logging, uranium mining, neutron activation analysis, isotope production or other applications that require a neutron source.

The invention provides a motor fault diagnosis method for deep learning network of data fusion. A neural network includes a data compression network, a feature extraction network and a classificationnetwork. A determination and training methods comprises the steps of (1) collecting current signals of A and B phases of a motor and a vibration signal of a motor end bearing, performing data standardization, obtaining a spectrum sequence by Hilbert-Huang transform, and establishing a data set of the neural network, (2) establishing a deep neural network, determining a network structure and initializing parameters, (3) inputting a training set into the neural network, calculating loss functions of different neural networks, and updating neural network parameters by using a loss value, and (4)inputting the data of a test set into a neural network, calculating an accurate rate, and repeating the step (3) until the accurate rate satisfies a requirement, wherein the neural network can map inputted current and vibration data to a feature plane after training, and a classification network can predict whether the motor is failed according to a fault state corresponding to a region where thenetwork is located.

The invention relates to a fast multi-focus image fusion algorithm. The algorithm comprises the following steps of firstly, decomposing a source image by using a smoothing filter and constructing thedecision graph model of multi-focus image fusion; secondly, using the correlation between the neighborhood pixels of multi-focus images to provide a fast steering filter optimization decision graph sothat calculating complexity is greatly reduced; then, reconstructing two scales of images; and finally, using the fusion method of the invention and other triple existing fusion methods to fuse the multi-focus images. Compared with the other three kinds of fusion methods, by using the method of the invention, regardless of subjective visual and objective evaluation indexes, the texture information, the significance information and other details of the multi-focus images can be preserved.

The present invention belongs to the technical field of information safety, and discloses a privacy protection multi-side diagnostic model fusion method and system, and a cloud server. The system comprises a credible mechanism, a cloud server and a medical center terminal. The credible mechanism completes system initialization to provide system parameters and secret key distribution for the medical center terminal and the cloud computing server, the cloud computing server stores encrypted data resources and fuse local diagnostic models to generate a global diagnostic model, the medical centerterminal generates the local diagnostic models, sends the local diagnostic models through encryption to the cloud computing server and is used for fusion computing task published by the cloud computing server, and the medical center terminal and the cloud computing server can perform bidirectional authentication in the service. The privacy protection multi-side diagnostic model fusion method and system, and the cloud server can achieve privacy protection of the local diagnostic models and case data resources of the medical center, can improve the system efficiency and can be used to provide convenient and private online medical service for users in the real life.

The present invention describes therapeutic agents for chemotherapeutic agent-resistant cancers that include an HLA class I-recognizing antibody as an active ingredient. A further objective of the present invention is to provide methods for treating chemotherapeutic agent-resistant cancers that include the step of administering an HLA class I-recognizing antibody to a subject. It is herein demonstrated that cytotoxic activity due to C3B3 diabody is induced at a lower concentration in chemotherapeutic agent-resistant hematological tumor cell lines having high MDR1 and HLA class IA protein expression, than in their parent cell lines. It was also found that when the chemotherapeutic agent-resistant hematological tumor cell line is pretreated with a C3B3 diabody, cell injury associated with sole use of chemotherapeutic agent is enhanced and the amount of pharmaceutical agents taken up into cells are increased. More specifically, it was discovered that the C3B3 diabody exhibits anti-tumor activity on MDR1-expressing tumor cells having high HLA class I expression, and thereby is effective in overcoming drug resistance.

The invention discloses a medical image fusion method based on an improved pulse coupling neural network. The medical image fusion method comprises the following steps of 1, performing Gamma correction on a multi-mode medical image to enhance the contrast ratio of the medical image; 2, performing multi-scale decomposition on the corrected image to be fused by adopting non-subsampled shear waves toobtain a low-frequency subgraph and a high-frequency subgraph; 3, fusing the low-frequency sub-graphs by adopting an improved regional energy algorithm; 4, fusing the high-frequency sub-graphs by adopting an improved pulse coupling neural network algorithm; and 5, reconstructing the fused high-frequency subgraph and the fused low-frequency subgraph by adopting non-subsampled shear wave inverse transformation to obtain a final fused image. The multi-modal medical image fusion method can effectively fuse multi-modal medical images, and improves the accuracy of doctors in diagnosing the illnessstate of a patient.