59 results about "Geometric standard deviation" patented technology

In one aspect, the process includes providing a precursor medium comprising a liquid vehicle and a precursor to a component, and flame spraying the precursor medium under conditions effective to form a population of nanoparticles, wherein the nanoparticles include the component. The population of nanoparticles, as formed, comprises less than about 5 percent by volume particles having a particle size greater than 1.0 μm. A size distribution of the population of nanoparticles may have a d50 value less than about 500 nm, and it may be unimodal. The size distribution may have a geometric standard deviation of less than about 2. The process may occur continuously for at least four hours or more. Greater than about 90 percent by weight of the precursor to the component in the precursor medium may be converted to the component in the nanoparticles. The process typically occurs in an enclosed flame spray reactor.

The invention relates to a generating device of quasi-monodispersed submicron aerosol by using an evaporation condensation method. The generating device comprises a high-pressure gas source, a first filter, a sprayer, a diffusion desiccator, a second filter, a saturator, a reheater, a condenser tube, and a first valve as well as a second valve, wherein, the first valve and the second valve are respectively used for adjusting the flow capacity of the second filter and the saturator; the high-pressure gas source adopts a high-pressure air source; the reheater and the condenser tube adopt metallic conduits; a baffle plate is arranged at a jet exit of the sprayer; a screen mesh or a closing ring is arranged at the inlet of the condenser tube. The generating device is convenient and durable, the running cost is low, the median diameter in aerosol generating counting ranges from 0.08 micrometres to 0.5 micrometres, the geometric standard deviation is less than 1.3, the counting concentration ranges from 1*10<4> to 1*10<8>/cm<3>, the stability and the repeatability are good when in generation, and the requirements of various equipment inspections and experimental researches can be satisfied.

The disclosed image process method for obtaining more accurate noise standard deviation or variance comprises: blocking the image frame to obtain the standard deviation or variance of small block, or, blocking the frame with its neighbor reference frame to obtain standard deviation or variance of every difference block; counting number of the block with same standard deviation or variance, and determining the noise standard deviation or variance according to the standard deviation or variance of block with the greatest number.

A method for optimizing centroid measurement precision of Hartmann wavefront sensor includes the steps of: performing threshold processing to pixel data outputted by photoelectric detector; calculating order moment centroid of each subaperture spot using the pixel data of threshold processing, wherein alpha=1, 2, 3......L; calculating repeatedly the order moment centroid of each subaperture spot; calculating the mean value of order moment centroid standard deviation of each subaperture spot; finding out the corresponding minimum order, namely finishing the method for optimizing centroid measurement precision of Hartmann wavefront sensor. Repeatedly calculating alpha order moment centroid of each subaperture spot and obtaining corresponding centroid standard deviation, thereby obtaining the excellent corresponding order which the mean value of all subaperture centroid standard deviation is small, and the filtered high-order centroid algorithm is capable of suppressing more effectively the influence of noise to centroid computational accuracy with the most excellent repeatable accuracy of centroid measurement relative to other each order moment centroid algorithm, thereby improving wavefront reconstruction accuracy.

A toner is provided. The toner includes toner particles, and silica particles and inorganic fine particles that are externally added to the toner particles, the inorganic fine particles having an average primary particle size smaller than that of the silica particles. The toner particles have a shape factor SF-1 of 130 or more and 140 or less, a shape factor SF-2 of 120 or more and 130 or less, and a volume average particle size of 5 μm or more and 8 μm or less. The silica particles have an average primary particle size of 80 nm or more and 150 nm or less, and an amount of water of 1.5% by weight or less. A particle size distribution of the silica particles is a logarithmic normal distribution, and a value of geometric standard deviation σ_g of the particle size of the silica particles is less than 1.30.

An anti-tussive nebulized solution for targeted delivery of lidocaine into conducting and central airways. A method for treatment of cough and tussive attacks or episodes using said lidocaine solution. A nebulized lidocaine solution administered in daily dose from about 10 mg to 80 mg of lidocaine dissolved in a saline and nebulized into an aerosol having a mass median aerodynamic diameter 3 μm to 10 μm and a geometric standard deviation less than 1.7 using an electronic nebulizer.

An imaging apparatus 1A includes an electron multiplying solid-state image pickup device which has an electron multiplier section for multiplying charge signals generated in respective pixels; a multiplication gain setting part 41 for setting a multiplication gain in the electron multiplier section; a standard deviation calculator 34 for calculating a noise standard deviation of a noise image acquired under a predetermined condition by the image pickup device; a reference standard deviation storage 35 storing a reference standard deviation, and a standard deviation comparator 36 for comparing the noise standard deviation and the reference standard deviation and outputting an obtained comparison result. At the time of gain adjustment, the multiplication gain setting part 41 adjusts the multiplication gain based on the comparison result by the standard deviation comparator 36. Thereby, an imaging apparatus which enables the user's side to easily and accurately re-adjust the multiplication gain of charge signals in the electron multiplying solid-state image pickup device, and a gain adjusting method for the same are realized.

The invention discloses a quantitative evaluation method of aquatic plant species for repairing eutrophication water. The quantitative evaluation method comprises the following steps: A, index screening, namely screening to-be-invested water physical and chemical indexes according to eutrophication water characteristics; B, field investigation, namely investing rivers or lakes by adopting a conventional method in a representative region range, recording names of all the aquatic plant species in a quadrat, collecting water samples, and determining physical index of water; C, standard non-dimension disposal calculation, namely classifying the aquatic plants into submerged plants, floating-leaved plants and emergent aquatic plants according to ecotypes, and calculating standard non-dimension disposal data of physical and chemical indexes of water to which each species corresponds according to ecotypes; D, weight giving and total standard deviation calculation, namely giving weight to the physical and chemical indexes, and calculating the general average and total standard deviation of the non-dimension disposal data of each species; E, sorting and quantitative evaluation. The quantitative evaluation method is simple and convenient to operate, fast and accurate, meets the wetland rejuvenation of the rivers, lakes and the like and can be used for promoting the high-efficiency restoration of wetland ecological environments and configuration of functional groups.

The invention relates to a method for identifying geometric deviations of a real motion guide from an ideal motion guide in a coordinate-measuring machine having a sensor for measuring a workpiece, or in a machine tool having a tool for processing a workpiece, wherein the coordinate-measuring machine or the machine tool has a movable part which is guided along the motion guide and by the motion guide.

A self-adaptive SAR image categorization method based on local standard deviation is provide, which comprises: (1) according to an original data, calculating a local standard deviation s (i, j) for the neighborhood of certain point (i, j); (2) carrying out self-adaptive selection of a GLCM calculation window by the local standard deviation. The specific procedures are: setting the threshold value of s as sth; judging whether the certain point s (i, j) is larger than the threshold value sth; if the certain point s (i, j) is larger than the threshold value sth, selecting a large size window for the calculation of GLCM; if the certain point s (i, j) is smaller than the threshold value sth, selecting a small size window for the calculation of GLCM; (3) setting other parameters of GLCM and calculating the GLCM of the certain point according to the size of window; (4) extracting a characteristic quantity from GLCM to compose an eigenvector and categorize the characteristic vector by adopting a c mean value clustering algorithm. Therefore, the invention can enhance the categorization accuracy of larger military objects.

The invention discloses a target detection method based on local standard deviation and Radon transformation. The method comprises the following steps: (1) inputting a color image to be detected; (2) preprocessing the image; (3) acquiring different grayscale background profile images; (4) performing differential operation; (5) performing Radon transformation; (6) correcting a maximum value curve; (7) performing Radon inverse transformation; and (8) outputting a target detection result. According to the target detection method based on the local standard deviation and the Radon transformation, the defects that the complexity of the algorithm is too high, and false-alarm points are easily generated around the target when a point target is relatively weak in the prior art can be overcome well, and the efficiency and accuracy of image target detectioncan be improved.

The invention provides a road network evaluation method based on a Bayesian model and a triple standard deviation criterion. The method comprises the following steps: the traffic indexes in the same period of time of a certain area are counted, and the probability distribution obeyed by the traffic indexes is determined according to the statistical result; calculating parameters of the probabilitydistribution by using a Bayesian model; according to the Bayesian model and the criterion of triple standard deviation, the range threshold of traffic index is calculated, and whether the road network is abnormal or not is judged by comparing the traffic index data with the range threshold of traffic index. If you determine that that road network is abnormal, according to the criterion of triplestandard deviation to calculate the change value and the change rate of network traffic flow, and the change value and the change rate of the traffic congestion index. As an index for evaluating the influence degree of the abnormal event on the road network, the invention can effectively identify the abnormal event of various road networks and the influence degree of the abnormal event on the traffic flow and the traffic congestion of the road network.

The invention belongs to the field of environmental organic pollutant analysis method application and particularly relates to a pretreatment process of using a bimetallic composite oxide nanomaterialwith a special shape and a large specific surface as an accelerated solvent extraction filler for determination of multiple persistent organic pollutants in an atmospheric particulate sample. According to the pretreatment process, an isotope dilution mass spectrometry method is adopted for detection, the standard recovery rate of the multiple persistent organic pollutants in the particulate sampleis within the range of 80.0-120.0%, and relative standard deviations are all within the range of 0.5-7.0%. The pretreatment analysis method is rapid, accurate and effective, meets the requirement forgreen chemistry and can be applied to routine detection of multiple types of organic matter in atmospheric particulates.

The invention discloses a passenger transport hub area resident number change trend short-time prediction method based on a kNN algorithm. The method comprises the following steps of obtaining passenger transport hub area resident situation data in real time through a detection system; according to the date characteristics of the to-be-predicted day, selecting m historical samples similar to the to-be-predicted day as predicted sample spaces; preprocessing abnormal data and noise in the historical sample; determining a characteristic space of the to-be-predicted day and the historical sample corresponding to the to-be-predicted time period, calculating an increment ratio standard deviation of the data of the historical sample and the to-be-predicted day on the characteristic space, and finding out k-day data with the minimum increment ratio standard deviation as k adjacent samples; calculating an increment ratio coefficient of k adjacent samples, and predicting the change trend of thenumber of resident people in the region according to the increment ratio coefficient; and calculating a short-time prediction value of the number of resident people in the region by taking the currentnumber of resident people in the region as a reference. According to the method, the change trend of the number of resident people in the short-time area can be accurately predicted by using the historical data, so that a high-precision short-time area resident number prediction result is obtained based on the calculation of the number of resident people in the current area. The method is suitable for the intelligent traffic field.

The invention discloses a light scattering-based particulate matter concentration detection device. The light scattering-based particulate matter concentration detection device comprises a particulate matter sampling and cutting device, a scattered light intensity detector, a circuit board, and a display device; the particulate matter sampling and cutting device is connected with the scattered light intensity detector; cut diameter of the particulate matter sampling and cutting device is 2.5+/-0.2<mu>m, and collection efficiency geometric standard deviation is 1.2+/-0.1<mu>m; the circuit board is provided with a data processing module, and a mass concentration conversion coefficient externally arranged interface; the data processing module is connected with the scattered light intensity detector; the mass concentration conversion coefficient externally arranged interface is connected with the data processing module; and the display device is connected with the data processing module. The light scattering-based particulate matter concentration detection device is convenient for people to obtain PM2.5 concentration at a certain time timely, and is capable of providing convenience for air quality PM2.5 index real-time monitoring, and air pollution degree evaluation of certain areas, and providing favorable conditions for active treatment of PM2.5 pollution.

The invention provides a definition method specification of a control specification limit in a statistical process control (Statistical Process Control, SPC), which comprises the following steps: step a, a sample group is provided; step b, k groups of bootstrap samples groups are chosen from the sample group in a bootstrap resampling way; step c, the average value and the relative standard deviation of the k groups of bootstrap sample groups are calculated from the k groups of bootstrap resampling sample groups; and step d, a control average value and a control standard deviation are calculated from the k groups of sample average value and relative standard deviation, wherein the control specification limit is the function of the control average value and the control standard deviation.

The invention provides a three-dimensional variational assimilation method for satellite observation data considering channel correlation. The method comprises the following steps: 1) diagnosing channel correlation and an error standard deviation of satellite observation errors by using posterior information; 2) performing diagonalization decomposition on satellite observation error covariance matrix block; 3) inversing the satellite observation error covariance matrix block; 4) constructing a three-dimensional variational assimilation cost function considering channel correlation of the satellite observation data; 5) solving the constructed cost function and a gradient thereof by using a conjugate gradient minimization algorithm, and obtaining an optimal analysis field; and 6) testing theconvergence of the constructed three-dimensional variational assimilation method. According to the method, the channel correlation and error standard deviation of the satellite observation data are diagnosed by using the posterior information, an observation error covariance matrix in three-dimensional variational assimilation is constructed, and a matrix inversion method is designed, thereby solving the problem that the correlation between satellite observation data channels is not considered in current three-dimensional variational assimilation.