34results about How to "Simple function" patented technology

The invention discloses a multi-component universe apparent resistivity defining method based on multiple-radiation-field-source transient electromagnetic method. The corresponding multi-component universe apparent resistivity defining method is established on the basis of the multiple-radiation-field-source air-ground transient electromagnetic method theoretical system. The system analyzes the advantages for carrying out universe apparent resistivity defining of a multiple-radiation-field-source air-ground system by utilizing magnetic field intensity, a respective universe apparent resistivity algorithm is put forward for each component of the magnetic field intensity, multi-component, full-time-domain and full-airspace apparent resistivity calculation is achieved, and influences of the offset distance on the universe apparent resistivity are analyzed. A multiple-radiation-field-source transient electromagnetic air-ground system can reinforce signal intensity and weaken random disturbance, and the positions of underground abnormal bodies can be recognized better by adjusting the relative positions between sources. The effectiveness of the multi-component universe apparent resistivity algorithm of the multiple-radiation-field-source air-ground system is verified through treatment on a designed model, and the advantages, of increasing exploration depth and improving resolution, of the multiple-radiation-field-source air-ground system are verified.

The invention discloses a human thoracic and abdominal surface area respiratory signal period prediction method based on variance calculation, and belongs to the technical fields of oncology, precision instruments, engineering technologies, mathematics and the like. The method comprises the following steps: firstly, constructing an ideal single-cycle respiratory signal, then carrying out cycle continuation to obtain an ideal multi-cycle respiratory signal, finally, carrying out cycle extraction on the ideal multi-cycle respiratory signal, and carrying out the whole extraction process based onvariance calculation of each imaginary cycle. The method can accurately extract the cycle of the ideal multi-cycle respiratory signal, and lays a simulation experiment foundation for accurately predicting respiratory movement.

The invention relates to the technical field of bioinformatics, and particularly discloses a device for noninvasive prenatal detection of chromosome abnormality. The device comprises a detection module, a data quality control module, a data preprocessing module, a calculation module and a judgment module, wherein the calculation module comprises a chromosome heterozygosis ratio calculation unit, achromosome adaptive correction standard deviation calculation unit and a chromosome Z value calculation unit; and the chromosome adaptive correction standard deviation calculation unit is used for carrying out logistic regression according to the relationship between the sequencing depth of a reference set sample and the standard deviation of the chromosome heterozygosis ratio to obtain a fittingfunction, and substituting the sequencing depth of a pregnant woman sample to be detected based on the fitting function to obtain the chromosome adaptive correction standard deviation of the sample to be detected. The device can improve the detection rate of chromosome abnormal true positive samples.

The invention discloses a thoracic and abdominal surface area respiratory signal period prediction method combining double-period judgment, and belongs to the technical fields of oncology, precision instruments, engineering technologies, mathematics and the like. The method comprises the following steps: firstly, constructing an ideal single-cycle respiratory signal, then carrying out cycle extension to obtain an ideal multi-cycle respiratory signal, and finally carrying out cycle extraction on the ideal multi-cycle respiratory signal, the whole extraction process is carried out in two imaginary cycles with the same data volume, and the cycle extraction can be completed in the two cycles; the method can accurately extract the period of the ideal multi-period respiratory signal, and lays asimulation experiment foundation for accurately predicting respiratory movement.

The invention discloses a table with a rotary placing frame. The table comprises a table top, table legs arranged below the table top, a first rotating placing mechanism and a second rotating placing mechanism. The first rotating placing mechanism comprises a first placing rod, a first rod groove, a first clamping column and a first protruded rod. One end of the first placing rod is rotatably arranged on the table top, a first placing plate is rotatably arranged at the other end of the first placing rod, the first rod groove is disposed in the first placing rod, and the first clamping column adaptive with the size of the first rod groove is rotatably arranged at one end of the first rod groove. When space is not roomy enough, the table can play a role of a dining table and can further serve as an office table. The table is simple in function and not large in size. The placing frame has different using functions at different set rotating angles. Office people can also perform operation simply when working from home.

The invention discloses a cellular network base station state time-varying model establishing method based on a Bayesian network. The cellular network base station state time-varying model establishing method comprises the following steps of (1) using the existing actual cellular network as a scene, sensing states of a base station switch in a system model by using secondary sensing equipment in a cellular network, collecting sensing data and forming an observation sequence; (2) creating a Bayesian network model by using the observation sequence and learning the model according to a Bayesian structure learning algorithm of a totally connected graph and condition mutual information to obtain a value of a dependency relation between a conditional probability chart and nodes; and (3) establishing a time-varying statistic model of the states of the cellular network base station by using the value of the dependency relation between the conditional probability chart and the nodes. By the cellular network base station state time-varying model establishing method, the problem that the existing method is high in complexity and cannot be adaptively adjusted along with change of the nodes of the network is solved, by the base station state time-varying model with low complexity, data business collision probability of master mobile users of a cellular network is reduced effectively, and data transmission efficiency in the network is improved.