264 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 present invention relates to a method and apparatus, based on the use of a neural network (NN), for (a) predicting important groundwater / surface water output / state variables, (b) optimizing groundwater / surface water control variables, and / or (c) sensitivity analysis, to identify physical relationships between input and output / state variables used to model the groundwater / surface water system or to analyze the performance parameters of the neural network.

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.

A system and method for assigning feature sensitivity values to a set of potential measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis is disclosed. Data is received from a sensor that represents a particular medical measurement. The received data and context data are analyzed with respect to one or more sets of training models. Feature sensitivity values are derived for the particular medical measurement and other potential measurements to be taken based the analysis, and the feature sensitivity values are outputted.

A data collection system collects and stores physiological data from an ambulatory patient at a high resolution and / or a high data rate (“more detailed data”) and sends a low-resolution and / or downsampled version of the data (“less detailed data”) to a remote server via a wireless network. The server automatically analyzes the less detailed data to detect an anomaly, such as an arrhythmia. A two-tiered analysis scheme is used, where the first tier is more sensitive and less specific than the second tier. If the more sensitive analysis detects or suspects the anomaly, the server signals the data collector to send more detailed data that corresponds to a time period associated with the anomaly. The more specific second tier analyses the more detailed data to verify the anomaly. The server may also store the received data and make it available to a user, such as via a graphical or tabular display.

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).

Embodiments of the invention provide a method, system and computer program product for carbon management for sourcing and logistics. In one embodiment, the method comprises using a computer for quantifying both a cost and a carbon impact of one or more logistics policies relating to a manufacturing process; and minimizing the cost and carbon impact using a defined equation including a first component representing a transportation cost, and a second component representing a carbon cost. In an embodiment of the invention, the quantifying includes using an analytics engine to quantify the cost and carbon impact. The analytics engine may include a shipment analysis module to calculate an optimal transportation policy, a sourcing analysis module for testing alternate sourcing options, a scenario analysis module to find an optimal order frequency, and a sensitivity analysis module to test the impact of various changes.

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.

An embodiment of the invention provides a pollution risk grading method for a landfill site. The pollution risk grading method for the landfill site comprises the steps of obtaining basic data of multiple pollution risk grading indexes of the landfill site and calculating the correlation among the risk grading indexes according to the basic data of the risk grading indexes and a risk grading index evaluation system to ensure the independence of the indexes; determining weight values of the risk grading indexes and performing sensitive analysis on the weight values of the risk grading indexes to ensure that the weight values of the risk grading indexes are accurate; and inputting the weight values of the risk grading indexes into a multi-criteria decision analysis model to calculate a risk value of landfill site pollution so as to perform risk grading scoring of landfill site pollution. Correspondingly, an embodiment of the invention furthermore provides a pollution risk grading apparatus for the landfill site. According to the technical scheme provided by the embodiments of the invention, the groundwater pollution risk of a hazardous waste landfill site can be subjected to grading evaluation quickly and accurately, so that a risk grading result is more scientific.

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 an analysis method for influence factors of carbon dioxide concentration prediction at any time-space position. The method comprises the following steps of: on the basis of sparse and non-uniform satellite carbon dioxide column concentration observation data, corresponding environmental factors including a ground cover factor, a climate and weather factor and a combustion emission factor, and an XGBoost algorithm, constructing a model for simulating spatial and temporal distribution of carbon dioxide in a region, and extracting carbon dioxide temporal and spatial distribution trend variables; and realizing quantitative evaluation of environmental factor sensitivity by utilizing the constructed regional carbon dioxide spatial and temporal distribution model and a global sensitivity analysis algorithm, and quantitatively determining influence degrees and magnitudes of various environmental factors influencing regional carbon dioxide spatial and temporal distribution according to a sensitivity analysis result. Compared with a traditional method, the method can simulate regional carbon dioxide concentration distribution with high precision, and achieve quantitative evaluation of the importance degrees of the environmental variables.

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.

An active power distribution network information physical system reliability evaluation method considering information failure comprises the steps: constructing a typical framework of an active powerdistribution network information physical system CPS, wherein a physical system comprises primary equipment and a distributed energy source DG, and an information system is divided into an applicationlayer, a communication layer and an interface layer; establishing a DG and load model, an information physical element model and an information transmission reliability model; in combination with theindirect effect of information failure on the power distribution self-healing process, quantitatively analyzing the influence of failure of an information system application layer, a communication layer and an interface layer on fault isolation, positioning and power supply recovery in an uncertain environment; employing a sequential Monte Carlo method and a non-sequential Monte Carlo method to sample a physical element and an information element respectively; by means of example simulation, carrying out the sensitivity analysis from the aspects of distributed power supply capacity, island operation mode, information element and information transmission abnormity and access network structure, wherein the accuracy and effectiveness of the method are verified through the obtained result, and suggestions and guidance are provided for construction of the active power distribution network.

The present invention discloses an offshore and shallow sea environment monitoring mooring system and a design method thereof. The system comprises an anchor, an anchor chain, a steel drum, steel pipes and a buoy which are connected in turn. A steel ball is also connected below the steel drum, a device used for detecting ocean data and communicating with the land is provided in the steel drum, and universal joints are used for connecting the buoy with the steel pipes, the steel pipes with the steel drum, and the steel drum with the steel ball. The research steps of the design scheme of the offshore and shallow sea environment monitoring mooring system are as follows: (1) the offshore and shallow sea environment monitoring mooring system is subjected to static equilibrium analysis; (2) a multi-objective optimization model is established; and (3) the model is verified and simplified by the sensitivity analysis result, to obtain a mooring system target optimization model for practical use.

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.

A computer implemented method for performing a path-sensitive analysis of a computer program using path-insensitive techniques employing an elaboration of the program which advantageously permits a correctness determination of the program as well as a simplification and optimization.

The invention provides a parameter uncertainty analysis method and system for a nuclear power reactor system, and the method comprises the steps: building a system evaluation model according to a system design value through employing an optimal estimation program, and carrying out the steady-state debugging of the system evaluation model; carrying out sensitivity analysis on the input parameter / physical model by utilizing a system evaluation model to obtain a key input parameter / key physical model, and obtaining a target change range of the key input parameter / key physical model according to characteristics of the key input parameter / key physical model; constructing a target parameter sample point set according to a target change range of the key input parameter / key physical model, and performing uncertainty analysis on the target parameter sample point set by using a system evaluation model to obtain an uncertainty quantification result of the key input parameter / key physical model, according to the method, the excessive conservative allowance of the nuclear power reactor system is effectively released, and the economical efficiency of the nuclear power reactor system is improved to the maximum extent.

The invention provides an automobile engine performance prediction and auxiliary calibration method and system, and the method comprises the steps: M1, collecting engine calibration experiment data, and selecting a researched engine control parameter and a corresponding engine performance expression parameter; M2, establishing an active subspace prediction model according to the selected researched engine control parameters and the corresponding engine performance expression parameters; and M3, substituting the engine control parameters under the preset working condition into the active subspace prediction model to obtain predicted engine performance expression parameters so as to achieve an auxiliary calibration effect, wherein the active subspace prediction model is a model based on datadimension reduction, and dimension reduction is carried out on a complex database. The active subspace model established by the invention can predict the operation condition of the engine to reduce the experimental workload on one hand, and can be used for sensitivity analysis of each control parameter of the engine to guide subsequent performance optimization work on the other hand.

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.

An aero-engine residual life prediction method based on transfer learning comprises the following steps: performing sensitivity analysis on multi-dimensional degradation parameters of an aero-engine, screening out degradation sensitive parameters, and preprocessing the degradation sensitive parameters to improve data expression ability; carrying out mobility measurement on a to-be-tested sample based on a dynamic time warping algorithm, and finishing time domain sensitive data matching by utilizing a sliding time window; on the basis of a mobility measurement result, screening a migratable sample and calculating a migration weight; constructing a training data set by cutting migratable sample data; training a plurality of residual life prediction models by using the training data of each transferable sample; and realizing prediction result fusion of the life prediction models based on the transferable weight to obtain a final prediction result.

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.