62 results about "Objective variables" patented technology

An estimation apparatus of an object appearance frequency is provided. The apparatus estimates an appearance frequency of objects, such as pedestrians, in a predetermined estimation area. The apparatus calculates a matrix FPFP by searching an appearance frequency data in the past. In detail, an estimated result that is an output of an estimating module is expressed as a vector. Objects to be estimated are classified into a total of 12 kinds, such as a “man, woman, child, bike, unknown, dog” and a “right, left”. Feature vector of appearance frequency of pedestrians, i.e., objective variables of estimation, is expressed by 12th dimension vector space. Moreover, status information is used as explaining variables, which explain the feature vectors when a feature vector occurs. The regression relationship of the feature vector with respect to the status information vector expressed in 28th dimension is solved by the linear least squares method.

Parametric Bayesian Updating method is described for attribute importance measures. AIM is a quantitative measure of the relative importance of multiple attributes used in a Bayesian Updating parametric multivariate modeling approach. The AIM is a function of the correlation coefficients between the attributes themselves and the attributes with collocated target variable. The importance calculation is straightforward, repeatable, and objective.

A linear regression equation is comprised of the objective variables and the explanatory variables. The objective variables are comprised of the L1 carrier phases, the L2 carrier phases, the C / A code pseudorange, the P(Y) code pseudorange, the clock error data, the delay data of the ionosphere, and the delay data of the troposphere that related with every satellite respectively. The explanatory variables are comprised of at least the integer ambiguity and a receiver's position.The receiver's position has been linear-approximated by using the estimation results of the passed receiver's positions. Applying a least square method to the regression equation, the integer ambiguity and the receiver's position is estimated.

The invention discloses a determination method for pressure altitude parameters on a subsonic vehicle body. The determination method comprises a selection principle of a pressure source on the subsonic vehicle body and a data processing method for measuring pressure based on a selected pressure source, wherein the selection principle of the pressure source on the subsonic vehicle body is used for selecting and locating the pressure source; the data processing method for measuring pressure based on the selected pressure source comprises a correction formula fitting and measured value returning and calculating method, wherein the correction formula comprises [alpha] correction, [beta] correction and static pressure correction, and the returning and calculating method is a method that objective variables are obtained according to the iteration of pressure measuring values. Test results show that the corrected data has very high reducibility, the objective variables including a flying attitude angle, a speed and the like of a subsonic vehicle with higher precision can be obtained by combining the returning and calculating method, therefore, a new method is provided for the subsonic flying speed measurement of the subsonic vehicle.

The invention discloses a complex industrial process data modeling method based on a dynamic convolutional neural network. The method comprises the following steps: process variables with strong correlation with industrial process objective variables are selected, and through sampling, a time sequence for each process variable is obtained; an equal depth box plot is used to carry out abnormal point detection and elimination on the time sequences, and a linear interpolation method is then used for filling; the time sequence for each process variable in a former process time delay range at the sampling moment of the objective variable is extracted, a two-dimensional matrix containing dynamic characteristics of the process is formed, and picture samples are formed; and the dynamic convolutional neural network is built to analyze the dynamic characteristics of the industrial process data, the time and space relation of each sensitive variable is recognized automatically, and a prediction model for the objective variable is built. A large amount of historical data accumulated in the actual production process field is used, a data model of predicting an unpredictable objective variable by using a predictable process variable is built accurately, and an important role is played in online production process evaluation, dynamic adjustment and even energy conservation and emission reduction.

A failure prevention diagnosis support system includes: an acquiring portion that acquires internal information about an internal state of an image forming apparatus; a storage portion that stores one or a plurality of logistic regression models that define an estimate value of a regression coefficient through a logistic regression analysis using the internal information obtained when the image forming apparatus is in a failed state and in a normal state; and a controller that performs a control operation to select a logistic regression model from the one or the plurality of the logistic regression models stored in the storage portion in accordance with the image forming apparatus, and to calculate risk degrees as objective variables that are indicators of failure degrees in the image forming apparatus by assigning the internal information acquired by the acquiring portion or the value obtained from the internal information to the selected logistic regression model.

The invention provides a visualization method for multivariable spatio-temporal data under a polar region projection mode. The visualization method comprises the following steps of: obtaining an objective variable and geographic region data; drawing an objective variable visualization image according to the objective variable; drawing a geographic region background according to geographic region data; and displaying an effect image according to the objective variable visualization image and the geographic region background. According to the method of the embodiment of the invention, three polar region projection conversion methods from a two-dimensional orthogonal longitude and latitude space to a two-dimensional polar region projection space are realized, a uniform vector representation mode and a drawing method in the two-dimensional orthogonal longitude and latitude space and the two-dimensional polar region projection space are supported, and auxiliary positioning information of the geographic region background is provided, therefore, the problem that polar regions in the two-dimensional orthogonal longitude and latitude space are discontinuous is overcome for a user, and the distribution situations of variables in polar regions can be observed and compared efficiently and interactively in real time.

The invention discloses a method and device for evaluating the operating state of a power transformer, belonging to the field of transformers, comprising: step 1: selecting an evaluation index for the operating state of the transformer, and constructing an evaluation model for the operating state of the transformer; step 2: dividing the operating state level of the transformer, and formulating the corresponding The scale interval of each operating state level; Step 3: Based on the analysis of the segmented triangular fuzzy number coupling set pair analysis, obtain the state deterioration connection degree of each evaluation index in the index layer; Step 4: Combined with expert experience, comprehensively apply fuzzy analytic hierarchy process and Similarity cluster analysis, assigning subjective constant weights to each evaluation index; Step 5: Determine the objective variable weights of each evaluation index based on the improved CRITIC weighting method, and combine the objective variable weights with the corresponding subjective constant weights; Step 6: According to the corresponding subjective and objective variable weights of the evaluation indicators, obtain the relationship degree of state deterioration between the project layer and the transformer as a whole. The invention can reasonably and accurately judge the running state of the transformer.

For a plurality of combinations of a plurality of operating states included in an operating range of an engine and for a plurality of combinations of a plurality of objective variables, a system, which calculates design variables, includes a calculating device for obtaining global optimal solutions of the design variables that minimize or maximize a sum of the plurality of objective variables, which respectively correspond to the plurality of combinations of the plurality of operating states; a receiving device for receiving designation of an upper limit or a lower limit of the plurality of objective variables; and a searching device for searching for global optimal solutions that result in the smoothest change in the design variables when changing the operating states in a range of the received upper limit to the received lower limit of the objective variables, with one of the global optimal solutions as an initial value set.

A prediction system capable of predicting an unknown value of an attribute with high accuracy is provided. Based on first master data, second master data, and fact data indicating a relation between a first ID which is an ID of a record in the first master data and a second ID which is an ID of a record in the second master data, the co-clustering means 81 co-clusters the first IDs and the second IDs. The prediction model generation means 82 generates a prediction model for each cluster of the first ID output from the co-clustering means 81. When the first ID and the objective variable which is one of the attributes included in the first master data are specified, the prediction means 83 predicts the value of the objective variable corresponding to the first ID based on the prediction model and the belonging probability that the first ID belongs to each cluster.

The invention belongs to the technical field of automobile industry data forecasting, and in particular relates to a method for forecasting electric vehicle ownership based on multiple linear regression and proportional substitution. Analyze the correlation with the target variable, establish and verify the multicollinearity model of traditional car ownership; according to the polynomial fitting results of the data of each influencing factor in known years, predict the unknown year data of each influencing factor, and substitute it into the above multiple After the collinear model, the traditional car ownership in the future years is predicted; combined with the local replacement ratio of electric vehicles and the actual growth of electric vehicles, new replacement increments are obtained, and then the total number of electric vehicles in the future years is predicted. The invention can use statistical data to calculate the number of traditional automobiles and calculate the number of electric vehicles, which is helpful for the planning of electric vehicle charging facilities and policy analysis.

Abnormal values of an objective variable are removed. A degree of association between an objective variable and a plurality of explanatory variables is calculated. A plurality of explanatory variables having a high degree of association are extracted. A degree of independence between the explanatory variables is calculated. A plurality of candidates of explanatory variables with a high possibility of having a great effect on the objective variable are selected based on the degree of association and the degree of independence. An explanatory variable having a high rate of contribution to the objective variable is selected from among the candidates, based on the cumulative contribution rate, and a regression equation is calculated to estimate a value of the objective variable. The same processing is repeated, using the difference as a new objective variable and explanatory variables except the explanatory variable used to obtain the difference as new explanatory variables.

A management server 10 stores past record data regarding the call volume by collecting from an exchanger, classifies the past record data in accordance with segments: a 7-day period in a month starting from the beginning of a month; a day of the week; and a predetermined period of time in a day, for a predetermined term in the past, obtains a multiple regression equation having the call volume as an objective variable, based upon the past record data thus classified, by performing a multivariate analysis for each day of the week, and calculates the call volume in a term that is specified by a user based upon the multiple regression equation.

Abnormal values of an objective variable are removed. A degree of association between an objective variable and a plurality of explanatory variables is calculated. A plurality of explanatory variables having a high degree of association are extracted. A degree of independence between the explanatory variables is calculated. A plurality of candidates of explanatory variables with a high possibility of having a great effect on the objective variable are selected based on the degree of association and the degree of independence. An explanatory variable having a high rate of contribution to the objective variable is selected from among the candidates, based on the cumulative contribution rate, and a regression equation is calculated to estimate a value of the objective variable. The same processing is repeated, using the difference as a new objective variable and explanatory variables except the explanatory variable used to obtain the difference as new explanatory variables.

The analysis method includes steps of acquiring measurement data obtained from swings and values of hitting results (flight distance or the like) (STEP 3); calculating a characteristic value (face angle or the like) from the measurement data (STEP 4); and determining an indicator for selecting a shaft of a golf club from the characteristic value and the values of hitting results (STEP 5). In the step of determining an indicator, when a hitting result of a golf club is an objective variable, and the characteristic value is an explanatory variable together with a predetermined shaft physical property (flex point or the like), and has a statistically significant relation with the hitting result, the characteristic value is determined as the indicator. A relational expression of the indicator and the hitting result is calculated for each value of the physical properties.

To provide a method which is aimed at correcting a machinery difference, that is capable of determining absorbance at the same wavelength in the respective apparatuses even in the case in which there is drift between measurement wavelengths between respective spectroscopic analysis apparatuses due to an individual difference in spectroscopes mounted thereon. Technical means for irradiating a light including a plurality of wavelengths onto an object to be measured, receiving a reflected light or a transmitted light from the object to be measured, determining absorbances at a plurality of wavelengths on the basis of information obtained by receiving the light, determining a second- or higher-order regression formula with the wavelengths of the light as explanatory variables and the absorbances as objective variables by use of the plurality of absorbances, and calculating absorbance at a specified wavelength by substituting a value of the specified wavelength for the regression formula. Further, it is also possible to calculate derivative absorbance at a specified wavelength by differentiating the regression formula and substituting a value of the specified wavelength for the differentiated regression formula.

An article residual value predicting device of the invention comprises an article residual value predicting computer, a first data memory device connected to the article residual value predicting computer to store, as basal record data, respective items such as article names, used article values for each article type, new article values for each article type, and year and month data to which the used article value is applied, a second data memory device connected to the article residual value predicting computer to store item category scores. The article residual value predicting computer comprises article residual rate proven-value calculating means for reading out the used article value and new article value for each article type stored in the first data memory device, calculating article residual rate proven-value from the ratio of the used article value to the new article value, and storing a calculated result thus obtained as an article residual rate proven-value in the first data memory device, category score calculating means for reading out the article name, article residual rate proven-value, year data to which the used article value is applied and month data to which the used article value is applied, which are stored in the first data memory device, and calculating an item category score by performing a regression analysis based on the qualification theory I using the readout article residual rate proven-value as an objective variable and the readout article name, the year to which the used article value is applied as an explanatory variable and the month to which the used article value is applied as an explanatory variable, and storing a calculated score thus obtained in the second data memory device, article residual rate predictive-value calculating means for reading out the score stored in the second data memory device with respect to a specified item category and adopting a year-classified score relative to the year at some future point to be predicted as the year-classified score to calculate an article residual rate predictive-value from an equation “(article residual rate predictive-value)=(item-classified score)+(year-classified score)+(month-classified score)+(constant value)”, and article residual rate calculating means for multiplying the article residual rate predictive-value by a new article value to calculate an article residual value. The first data memory device serves to store maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years. The article residual value predicting computer further comprises a first weight coefficient calculating means for reading out the maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years stored in the first data memory device, calculating a weight coefficient from an equation “(maker-classified new article sales quantity before elapsed years) / (maker-classified record number)” or “(article name-classified new article sales quantity before elapsed years) / (article name-classified record number)”, and storing the weight coefficient based on the calculated new article sales quantity in the first data memory device, and weighting means for reading out the weight coefficient based on the calculated new article sales quantity from the first data memory device and duplicating the number of relevant records stored in the first data memory device corresponding to the weight coefficient based on the readout new article sales quantity and storing the record numbers increased by duplicating. The category score calculating means serves to perform the aforementioned regression analysis using concurrently all the relevant records weighted by the weighting means collectively.

Systems and methods for estimating the opportunity in a reservoir system over different time horizons relative to the critical values of risk and opportunity and corresponding values of an objective variable.

A linear regression equation is comprised of the objective variables and the explanatory variables. The objective variables are comprised of the L1 carrier phases, the L2 carrier phases, the C / A code pseudorange, the P(Y) code pseudorange, the clock error data, the delay data of the ionosphere, and the delay data of the troposphere that related with every satellite respectively. The explanatory variables are comprised of at least the integer ambiguity and a receiver's position.The receiver's position has been linear-approximated by using the estimation results of the passed receiver's positions. Applying a least square method to the regression equation, the integer ambiguity and the receiver's position is estimated.

A data analysis support systems according to the present invention assumes any of multiple indices to be an objective variable, implements clustering and collectively outputs indices belonging to the identical cluster.

The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering processing for performing co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Prediction model generation means 72 performs prediction model generation processing for generating a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs. The prediction model generation processing and the co-clustering processing are repeated until it is determined that a prescribed condition is satisfied.

The object of the invention is to estimate a causal relation between predetermined data at high precision and at ease, taking nonlinearity between the data into consideration. A work improvement support device is comprised of a nonlinear term adding unit for calculating a nonlinear value as for respective working data in the working data group and adding the nonlinear value to a working data group, a multiple regression analysis unit for calculating a regression formula as for respective working data according to the multiple linear regression analysis, a data group setting unit for determining whether there is a linear term in the calculated regression formula and setting the predetermined data comprising the linear term and the objective variable of the regression formula as the same group, and an explanatory variable candidate selecting unit for selecting the working data, excluding the predetermined data, as the explanatory variable candidates for the multiple linear regression analysis.

Provided is an optimization system capable of creating a large amount of data for optimization and specifying values of control variables in order to acquire an optimum result in consideration of uncertainty of predictive values. A simulation means 2 is given a model which is information modeling an object to be analyzed therein and including a parameter containing predictive values and their error ranges, control variables and an objective variable, determines values of the control variables per simulation for specifying a value of the objective variable, and conducts simulation multiple times based on the model. Further, the simulation means 2 determines definite values of the predictive values based on a random number and the parameter per simulation, and conducts simulation by use of values of the control variables and definite values of the predictive values. A control variable value specification means 3 specifies values of the control variables when the objective variable takes an optimum value.