259 results about "Sensitive analysis" patented technology

An advanced Geographic Information System (GIS) providing modeling, analysis, Multicriteria Decision Analysis (MCDA), data visualization, data sonification, web interface, risk management, sensitivity analysis, and telemetry capabilities is described. Telemetry can be provided by field sensors, field video, and/or field audio, and can be recorded or provided live in either direct or analyzed form. Data visualizations can be displayed along with GIS graphics, and can be superimposed or otherwise integrated with GIS graphics. GIS graphics can be used as the basis for an interactive user interface for other parts of the system. Web interfaces can support remote use, server based implementations, and/or collaboration. Advanced multi-channel data sonification can be used to prevent visual overloading of GIS graphics and interactions with it. The system can include rich data processing capabilities and interconnections of these so as to facilitate powerful data visualization and sonification results and can support multidimensional user interface devices.

The invention relates to a method for detecting the plasticizer content in a solid sample by headspace solid phase microextraction gas chromatography-tandem mass spectrometry, and the method combines an isotope internal standard and the headspace solid phase microextraction gas chromatography-tandem mass spectrometry (HS-SPME-GC-MS/MS) analysis for qualitative or quantitative analysis of a dimethyl phthalate plasticizer in a sample. The method mainly comprises the following steps of: a) mixing the solid sample with the isotope internal standard, and conducting extraction with headspace solid phase microextraction equipment; b) carrying out sample analysis; C) performing GC-MS/MS analysis; and d) implementing result treatment and quantitative analysis. The method of the invention is a fast, efficient, sensitive analysis method.

The present invention discloses the use of Allele-Specific-oligonucleotide (ASO) as a detection assay for human HLA classification. Using Reversed-Dot-Blotting format and flow through hybridization process, more efficient, faster and less expensive HLA classification can be achieved. A simplified procedure for HLA genotyping is also described. This invention further provides a Single Nucleotide Polymorphism (SNP)-based DNA fingerprining method for rapid and accurate genotyping, identification as well as DNA analyses of genetic data from human beings and different organisms. In addition this invention also discloses a new device for rapid and sensitive analyses of nucleic acids, proteins and other analysts for diagnosis.

The invention belongs to the field of tight sandstone gas reservoir development, and discloses a productivity prediction model and productivity sensitivity analysis method for a multi-section fractured horizontal well in a low-permeability tight gas reservoir. The model and method are characterized in that after a variety of factors, such as high-speed non-darcy effect, stress sensitivity, slippage effect, stratum pressure drop, in-fracture linear flow and in-fracture radial flow and shaft pressure drop are comprehensively considered, a steady-state seepage low model of the fractured horizontal well is provided; the influence of the various factors on a productivity calculation result is quantitatively analyzed; through example verification, for the multi-section fractured horizontal wellin the tight gas reservoir, the model can accurately predict the productivity; and a productivity sensitivity analysis result shows that the influences of horizontal well length, fracture half length,fracture number and fracture flow conductivity on the productivity have obvious limits, and a reasonable value should be selected from the perspective of economic development. The model and method have extremely high theoretical and application values for productivity prediction and productivity sensitivity analysis for the multi-section fractured horizontal well in the low-permeability tight gasreservoir.

The invention belongs to the gas-sensitive analysis system field and relates to an external temperature control type intelligent gas-sensitive analysis device. The device is characterized in that the device can realize direct analysis on the gas-sensitive properties of materials and realize independence of material gas-sensitive property research from sensor devices. The external temperature control type intelligent gas-sensitive analysis device provided by the invention comprises a test platform (1), a gas chamber (2), a control system (3), a cooling water circulation box (4), a vacuum pump (5), a dynamic gas distribution system (6) and test software (7); the test platform (1) is internally provided with a temperature control device (11), a probe adjusting device (12), a liquid evaporator (13), a fan (14), a lighting unit (15), a vacuum gauge (16), a vacuumizing channel (17), a gas path (18) and a temperature and humidity sensor (19); the temperature control device (11) comprises a sample test bench (111), a built-in heater(112), a temperature sensor (113) and a water cooling protection system (114) wrapping around the heater (112); the temperature control device (11) can directly carry out external temperature control on a test sample; the probe adjusting device (12) comprises an adjusting bracket (121) and an elastic probe (122); and a contact point of the elastic probe (122) and the test sample can be adjusted through the adjusting bracket (121).

Methods and systems for security analysis of an application are disclosed. One system includes a flow-insensitive analyzer, a control flow assessment module and a flow-sensitive analyzer. The flow-insensitive analyzer is configured to conduct a flow-insensitive analysis on the application to obtain a set of potential vulnerabilities in the application. In addition, the control flow assessment module is configured to determine, for each of the potential vulnerabilities, a relevant set of control flows that include the respective vulnerability. Further, the flow-sensitive analyzer is configured to perform, by a hardware processor, for each relevant set of control flows, a flow-sensitive analysis of at least one of the control flows in the corresponding relevant set to assess the validity of the respective vulnerability.

The invention relates to a nondestructive testing method for the adhesion of a thin film based on a cohesion model. The nondestructive testing method comprises the following steps: representing the adhesion between the thin film and a substrate by using a relation of an adhesion force between interfaces and an interface relative displacement; establishing a potential function of an index type cohesion model to represent an adhering condition between the thin film and the substrate; utilizing the index type cohesion model as a constitutive model of a thin film substrate interface unit in a thin film substrate structure and establishing a finite element model considering the interface adhesion, wherein the model includes two parts including the thin film and the substrate, and a cohesion unit is added between the thin film and the substrate; carrying out interface adhesion sensitive analysis on composite parameters to determine key parameters; calculating a theoretical frequency dispersion curve of transmitting ultrasonic surface waves in a layered structure when the adhesion between the thin film and the substrate is considered; acquiring an experiment frequency dispersion curve of the surface waves; obtaining a measured value of the adhesion of the thin film. The nondestructive testing method provided by the invention can realize quantitative characterization of the adhesion of the thin film.

The invention belongs to the field of shield underneath pass existing tunnel safety risk evaluation, and particularly discloses a shield proximity existing tunnel safety evaluation method based on data mining and data fusion. The method comprises the following steps: establishing a shield underneath pass existing tunnel safety evaluation index system and a risk grading standard; designing a Bayesian network by adopting a fault tree analysis method; obtaining an expert evaluation interval fuzzy set of a root node in the Bayesian network based on a group decision judged by an expert, and fusingthe expert evaluation interval fuzzy set by adopting an improved evidence theory to obtain fuzzy prior probability distribution of the root node of the Bayesian network; and based on the constructed Bayesian network model, carrying out risk probability reasoning and sensitivity analysis of shield underneath passing through the existing tunnel, and determining the safety risk level and key controlfactors of shield underneath passing through the existing tunnel. According to the method, more accurate and reasonable comprehensive evaluation of the safety risk of shield underneath passing throughthe existing tunnel is realized, and a powerful basis is provided for safety risk early warning and control decision of engineering.

The invention relates to a method of identifying illegal cooking oil by means of a Raman spectra technique and belongs to the technical field of food fast detection. The method comprises the following steps: measuring 2-5mL of edible oil needed to be detected, adding an extraction reagent A double in volume, whirling the mixture for 5-10min and then performing magnetic field assisted separation to extract natural capsaicin from the edible oil; then analyzing the extracted natural capsaicin by using 2-5mL of methanol to obtain a methanol extraction liquid; then adding the methanol extraction liquid into a derived solution B the same in volume, and performing a whirling reaction at room temperature for 1-3min; and finally adding an SERS enhancing reagent C the same in volume to the reaction solution, performing the whirling reaction at room temperature for 1-3min, and performing SERS detection; if characteristic peaks of natural capsaicin are shown in the SERS spectrogram, the detected edible oil is illegal cooking oil. The invention provides a simple, quick and super-sensitive analysis method for identifying illegal cooking oil, which has the characteristics of being distinct in target object, strong in characteristics, accurate in quality and simple in operation.

The invention discloses a local well pattern water injection development optimization method based on a graph neural network. The method specifically comprises the steps as follows: (1) obtaining geometrical information of a local target well pattern, and constructing a graph neural network structure of an injection-production well pattern according to the topological structure characteristics ofa water injection well and an oil production well; (2) acquiring historical production data of a target well pattern; (3) training the graph neural network structure in the step (1) by utilizing the historical production data in the step (2), and establishing a graph neural network model; and (4) performing sensitivity analysis on the parameters of the injection-production well pattern by utilizing the graph neural network model to obtain sensitivity factors in the injection-production well pattern. According to the method, the basis for adjusting the water injection amount is provided for water injection development, the oil production amount is predicted, meanwhile, the importance of different wells and different static parameters in each well pattern is given, and main influence wells and secondary influence well points are distinguished when the water injection amount is adjusted.

The invention discloses a pile-soil interaction prediction analysis method based on machine learning, and belongs to the technical field of foundation engineering. The method comprises the following steps: establishing a pile-soil variable parameter sample by adopting a Latin hypercube sampling method, modeling the parameter sample by adopting a numerical simulation method to obtain a stress deformation value of a pile body corresponding to the parameter sample, and performing sensitivity analysis on an input variable and a demand variable by adopting a Lasso method to reduce the dimension ofthe input variable; dividing the parameter samples into K parts with equal quantity for cross validation; establishing a BP neural network model based on an L-M algorithm, defining the number of neurons of the hidden layer within a certain range for cyclic traversal operation, determining the optimal number of neurons of the hidden layer by comparing training errors, and predicting the stress deformation of the pile body by using the trained neural network model. The method has the advantages of being clear in analysis process, high in reliability and high in efficiency, and a theoretical basis is provided for design and application of the pile foundation.

The invention provides a hierarchical modular power network planning scheme optimization method. The method includes the steps that (1) a county-level planning unit serves as a minimum unit of a calculation system of the method, and the calculation problem of the capacity-load ratio of an interconnected area is solved by means of division coefficients; (2) a calculation system is built on the basis of the method, and imbalance degrees, such as the largest load imbalance degree, the coincidence factor imbalance degree and the capacity-load ratio imbalance degree, of indexes in a target area are obtained quantitatively; (3) sensitivity of the capacity-load ratio to an item is analyzed, the concept of a sensitivity coefficient is put forward and accordingly influences of operation of the item on the capacity-load ratio of the area are weighed; (4) a load prediction method in a new planning period is put forward, according to the method, a hierarchical modular analysis concept is adopted, firstly, the whole load prediction work is divided into different levels of load prediction according to a certain rule, the natural growth rate of the load of each level is solved level by level, and ultimately the largest load of each level of a power grid at the end of the planning period is solved from bottom to top.

The invention discloses a hyporheic exchange influence factor sensitivity analysis method based on orthogonal experimental method. The method specifically comprises the following steps: firstly constructing a two-dimensional sand slope upper surface water-underwater exchange mathematical model based on Reynolds average N-S equation and k-Omega turbulent model, wherein the model comprises a two-dimensional sand porosity water flow steady movement equation, and establishing a solute migration model; and then setting a boundary condition for the surface water-underwater exchange mathematical model; and finally solving the surface water-underwater exchange mathematical model by adopting the orthogonal experimental method, taking the time consumed for changing the monitoring domain point soluteconcentration to the balance as an evaluation index of the sensitivity analysis, thereby analyzing the surface water-underwater exchange mathematical model and solute movement model parameter sensitivity. The problem that the prior art is large in hyporheic exchange sensitivity analysis workload and difficult to implement is avoided.

The embodiment of the invention provides a method and equipment for detecting an oil and gas reservoir by a compact oil and gas reservoir fluid factor. The method comprises the following steps: constructing a matrix modulus prediction model of compact sandstone according to an actual core sample of the compact reservoir and test data; constructing a rock physical model under a dry condition of thecompact reservoir by adopting a cemented sandstone theory according to an actually measured core porosity and an acoustic velocity; carrying out fluid replacement analysis and lame modulus related parameter conversion by adopting a Gassmann equation and combining a prediction result of the dry compact sandstone rock physical model, and carrying out fluid sensitivity analysis on lame modulus related parameters; determining an equivalent fluid factor according to attribute parameters of fluid replacement calculation in combination with actual drilling and logging data; acquiring an elastic parameter of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume in combination with logging information; and carrying out seismic fluid detection analysis according to the newly constructed seismic fluid factor, and predicting distribution of the seismic oil and gas reservoir.

The invention discloses a steel catenary riser grounding fatigue uncertainty analysis method. The method comprises the following steps: constructing the steel catenary riser system to perform riser fatigue analysis; performing sensitive analysis on the riser grounding point fatigue damage by importing the Morris method; and performing simulation study on the grounding point fatigue life uncertainty by adopting a Monte Carlo method. The steel catenary riser grounding point fatigue analysis precision is improved, and the analysis design of the steel catenary riser is more suitable for the engineering actual condition.

The invention discloses a rock-fill dam material compaction quality detection method based on soil rigidity, which comprises the following steps: in combination with the rolling characteristics of a rock-fill dam material, establishing a rolling machine-soil body vibration system three-degree-of-freedom kinetic analysis model capable of being applied to the rock-fill dam material, and obtaining the relationship between soil body parameters and the acceleration of the vibration system; obtaining the absolute value of the measured acceleration signal data of the vibration wheel, performing peak value selection and moving average, and obtaining the average amplitude a2m of the acceleration signal of the vibration wheel; by means of parameter sensitivity analysis, assuming that the soil body damping cs is always a damping value in an initial rolling state, and rapidly calculating the soil body rigidity to reflect the compaction quality of the rock-fill dam material in the vibration compaction process according to the obtained acceleration average amplitude a2m and the relation between the soil body parameters and the acceleration. The method disclosed by the invention can be applied to compaction quality field control and detection of fine materials such as clay core walls and the like, and also has a relatively good detection effect on coarse-grained materials such as rockfill materials and sand gravel materials.

The invention discloses an aircraft fire control system precision sensitivity analysis method based on a neural network. The method comprises the steps: introducing a local sensitivity algorithm and a global sensitivity algorithm into the index sensitivity analysis of an aircraft fire control system, and comprehensively employing an entropy method and a Sobol method according to the organic combination of different precision indexes, thereby achieving the precision sensitivity analysis of the aircraft fire control system. The analysis precision of the sensitivity coefficient of the precision index of the aircraft fire control system is improved. According to the method, the BP neural network is added in the aircraft fire control system and is trained, the precision index of the aircraft fire control system and the final killing probability are fitted, and required sample data are expanded quickly and effectively. Through a precision optimization distribution algorithm, optimization distribution of precision indexes of the aircraft fire control system is realized. According to the method, the problem of analysis and evaluation of multi-precision indexes in the aircraft fire control system under the condition of insufficient data samples can be well solved, experimental data can be quickly expanded based on learning and analysis of the neural network, and it is guaranteed that the aircraft fire control system reaches the specified killing rate.

The invention belongs to the field of atmospheric pollution prediction model construction, and particularly relates to an atmospheric O3 concentration prediction method based on comparison of multiplemodels. The method comprises the following steps: (1) acquiring target urban pollutant concentration data and related meteorological data; (2) preprocessing the original data; (3) determining a prediction model performance evaluation standard; (4) respectively constructing a corresponding O3-multiple linear regression model, a time sequence model and a BP neural network model for the target cityO3, and selecting the BP neural network model; (5) further optimizing the BP neural network, and determining an optimal BP neural network model; and (6) performing sensitivity analysis on the optimalBP neural network model.

The invention discloses a method for constructing a gasoline alternative fuel model and simplifying a mechanism. The method comprises the following steps: determining key components and an adaptationmechanism of a gasoline alternative fuel; systematically simplifying the framework of the single fuel mechanism from the perspective of framework simplification by adopting a directed relation graph (DRG) method, a directed relation graph (DRGEP) method based on error propagation, and a directed relation graph (DRGEPSA) method based on a full substance sensitivity analysis (FSSA) method and coupling error transfer and sensitivity analysis; coupling single fuel mechanisms obtained after respective simplification to obtain a constructed alternative fuel simplification mechanism; and selectingthermodynamic parameters, and comparing and verifying a constructed simplified mechanism model by utilizing comparison of experiments and numerical simulation. The method greatly reduces the sizeof the detailed mechanism, and can achieve efficient and accurate calculation.