64 results about "Categorical analysis" patented technology

Disclosed are systems and methods for on-line monitoring of operation of a process in connection with process measurements indicative of the operation of the process. In some cases, the operation of the process is simulated to generate model data indicative of a simulated representation of the operation of the process and based on the process measurements. A multivariate statistical analysis of the operation of the process is implemented based on the model data and the process measurements. The output data from the multivariate statistical analysis may then be evaluated during the operation of the process to enable the on-line monitoring of the process involving, for instance, fault detection via classification analysis of the output data.

The invention relates to a video monitoring image based method for automatically distinguishing the traffic states of roads, comprising the following steps of: detailedly classifying and analyzing video monitoring videos starting with technical levels, i.e. background modeling, foreground extraction, vehicle identification, multi-target tracking, and the like; and finally distinguishing the traffic states of the roads provided with monitoring cameras by setting corresponding parameter values. The invention greatly enhances the accuracy of the automatic identification of the traffic states.

The invention discloses a detection method of an abnormal event of multi-variable water quality parameter time sequence data. The detection method comprises the following steps: firstly, inputting a plurality of water quality parameter models; training and constructing a data driven predication model (BP model); analyzing multi-variable water quality time sequence data in a water supply pipe net and estimating the model; secondly, predicating through the BP model to obtain a predicated value of water quality data; comparing an actually measured value of a current state and the predicated value obtained by the predication model and carrying out error estimation and classification analysis, so as to determine a single-variable parameter abnormal event; classifying based on an error counting result, and updating and determining the event probability of single-variable water quality parameters through sequential Bayesian updating; carrying out multi-variable fusion decision-making and fusing information from a plurality of water quality monitoring indexes; providing a unified decision-making result and determining whether the water supply pipe net has the abnormal event on a specific node or not.

The composition of a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the model parameters using multiple analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). The analytics modeling component also includes an analytics taxonomy in which the analytical relations are categorized into related analytics categories. Navigation through the analytics taxonomy assists in the composition of an analytics model. The analytics taxonomy may, but need not, be domain specific.

Authenticating an authentic article having an authentication mark. Acquiring a set of spectral images of the authentication mark, for forming a set of single-authentication mark spectral fingerprint data (FIG. 1). Identifying at least one spectral shift in the set of single-authentication mark spectral fingerprint data, for forming an intra-authentication mark physicochemical region group including sub-sets of intra-authentication mark spectral fingerprint pattern data, such that data elements in each sub-set are shifted relative to corresponding data elements in remaining sub-sets in the same intra-authentication mark physicochemical region group (FIG. 2). Forming a set of intra-authentication mark physicochemical properties and characteristics data relating to the imaged authentication mark, by performing pattern recognition and classification analysis on the intra-authentication mark physicochemical region group (FIG. 3). Comparing and matching elements in the set of intra-authentication mark physicochemical properties and characteristics data to corresponding reference elements in reference set of data, thereby authenticating the authentic article.

The invention discloses a geology calamity early warning analysis system and method; the system comprises a database, a server, a data analysis module, a data acquisition module and an alarm; the dataacquisition module is linked to the data analysis module and the database through the server; the data analysis module comprises a classification analysis unit, a cluster analysis unit, an association rule analysis unit, a regression analysis unit, a vector similarity analysis unit and a time sequence analysis unit; the database comprises a basic geography database, a geology calamity core thematic database and a dynamic monitoring database; the system and method use the data analysis module to effectively analyze the calamity frequently occurring region geology information, thus obtaining the current calamity possible generation probability, and using the alarm to send alarms so as to remind work personnel if the probability reaches certain level. The system is easy to use, simple and fast, and the method can effectively analyze the geology calamities and warn people in advance.

The invention discloses a teaching system and method based on learning behavior analysis. The teaching system comprises a teaching server, a teacher terminal and student terminals. The teaching servercomprises a teaching resource server, a knowledge point test server, a question and answer interactive server, a learning behavior data acquisition server, and a learning behavior analysis server anda data visualization server. The learning behavior data acquisition server is used for collecting all kinds of behavior data produced caused by the student terminals visiting the teaching resource server, the knowledge point test server and the question and answer interactive server. The learning behavior analysis server classifies and analyzes all kinds of behavior data collected by the learningbehavior data acquisition server and displays the classification analysis results in the data visualization server. The teaching system and method based on learning behavior analysis acquires all kinds of learning behavior data of students in the learning process in real time, evaluates learning behavior data of each student by multi-dimensional quantification, and dynamically adjusts the teaching content according to the learning behavior analysis result, and a personalized teaching design for students is carried out.

The invention belongs to the field of proteomics classification, relating to a classification method of differential proteomics. The method comprises: selecting characteristics by univariate statistics analysis, sequential feature selection and a genetic algorithm; extracting characteristics by main ingredient analysis and a partial least squares method; connecting and integrating with linear discriminant analysis, a k-nearest neighbor classifier, a support vector machine, a decision tree, a naive Bayes classifier and an artificial neural network in series to obtain serial integrated classifiers which are connected in parallel and combined; endowing each base classifier with a weighting coefficient according to classification accuracy rate; and taking a fuzzy attribute value as a classification result output mode to obtain the classification judgment result of a target sample. The method has higher classification accuracy rate and robustness on classifying and judging abnormal and normal samples in the differential proteomics, and is suitable for classifying and analyzing the data of differential proteomics.

A method and system are disclosed for providing categorical analysis of user interactions within a social media environment. Social media interactions are monitored, collected, and processed to generate social network advocacy (SNA) analyses, which are in turn processed to categorize associated SNA data into predetermined SNA categories. Analysis operations are then performed on the resulting categorized SNA data to generate an SNA value for each of the SNA categories, along with associated statistical analyses, which are then used to provide the basis for proactive marketing response responses within one or more social media environments.

The invention discloses an electric power big data visualization oriented data mining method, comprising obtaining an electric power marketing data set A composed of n collected data samples: a1, a2, and the like, an; classifying the data clusters in the n data samples into K classes; mapping the classified data to a kernel space through a kernel function; determining the center of every cluster; constructing a kernel matrix corresponding to the data set A; reducing the scale of the kernel matrix; constructing class vectors for all data samples aj; rapidly calculating distances according to the reduced kernel matrix K * obtained in the previous step; updating the class vectors so as to redetermine the class attributions of the data samples aj. According to the electric power big data visualization oriented data mining method provided by the invention, effective clustering and classifying analysis is carried out to the marketing data; the user group is identified well; the classified user power consumption features are obtained; therefore, the foundation is laid for electric power marketing intelligence.

The invention relates generally to financial instrument classification and more particularly to methods and system for recognizing similarities in behaviors among financial instruments. According to one embodiment, a method of classifying similar financial instruments is provided. Classification analysis is performed on a desired financial instrument that a user specifies to determine other financial instruments that behave similarly to the specified financial instrument during a specified time range. Based on the classification, the similarly behaving financial instruments and additional characteristics are presented to the user for evaluation and tracking.

The invention discloses a power distribution real-time data validity analytical processing system and method. The system comprises an alarm server, a validity analysis server, a data validity historical alarm memorizer, an alarm window, a local alarm memorizer, a validity analysis client terminal and a validity statistical query component, wherein the validity analysis server, the data validity historical alarm memorizer, the alarm window and the local alarm memorizer are connected with the alarm server, the validity analysis client terminal is connected with the validity analysis server, and the validity statistical query component is respectively connected with the validity analysis client terminal and the data validity historical alarm memorizer. The validity analysis server is connected with the local alarm memorizer. Classification analysis is carried out on power distribution real-time data, valid data are judged for alarm display, filtration and statistics are carried out on invalid data, the invalid data are not sent into the alarm window, the statistical result is displayed through the validity analysis client terminal, and related defects of a power distribution system secondary device are eliminated by assisting the operation and maintenance staff.

The invention provides an express logistics process state detection multi-classification system. The system firstly executes a binary classification algorithm subjected to dimension reduction preprocessing through a microprocessor of an acquisition end to preliminarily judge whether the express items are in an abnormal state or not in the current time period; if the express items are in a normal state, covering the data; if the the express items are in the abnormal state, enabling the acquisition end to store the data to an acquisition end memory; and transmitting the calculated statistical characteristic data of the time period to the central processing unit through the communication module, further executing multi-classification analysis and abnormal degree judgment on the central processing unit, and enabling the central processing unit to input a processed result into the database for a client application end to inquire. According to the invention, accurate and detailed abnormal types can be obtained, so that real-time intelligent monitoring of the vibration state of the express logistics process is realized. Meanwhile, data such as temperature, humidity and illumination intensity are included in the detection range, so that the whole detection system is more scientific and perfect.

The invention discloses an InSAR-based effective area determination method for early dynamic identification and monitoring of regional landslides. Aiming at the complex terrain characteristics of landslide hazard environment and satellite side-looking imaging characteristics, based on the precise analysis of terrain factors such as slope, slope aspect, satellite imaging incident angle and azimuth,the method provides and establishes the comprehensive factors of terrain and satellite attitude data response relationship. The local incident angle of each monitoring point on the ground under the ascent and descent orbit mode is calculated and simulated, and the local incident angle of the ground is classified and analyzed. This method analyzes the interferometric characteristics of InSAR and the interferometric characteristics of InSAR, and the effective monitoring area and the overlapping and shadowing areas which can not be monitored effectively are determined accurately, so the accuratedetermination of the effective monitoring scope of the regional landslide identification monitoring under different complex terrain areas and different satellite side-looking imaging attitude parameters are realized, and the precise analysis of the early dynamic identification monitoring of the regional landslide by the time series InSAR technology is realized.

The invention discloses a convolutional neural network-based multivariate time series data classification method. The method comprises the steps of S1: obtaining multivariate time series data; S2: performing denoising preprocessing on the obtained multivariate time series data; S3: performing dimension reduction on the preprocessed multivariate time series data by adopting a convolutional neural network; and S4: performing segmentation on the data obtained by the dimension reduction by adopting a segmentation aggregation algorithm, calculating a Euclidean distance of the aggregated series data, and according to the Euclidean distance, defining a threshold for performing distinguishing and forming a classification result. The method has the beneficial effects that basic structure features of original multivariate time series data can be better reserved and can be subjected to classification analysis by adopting the segmentation aggregation method; the original multivariate time series data is subjected to dimension reduction representation by adopting the convolutional neural network; the result after the dimension reduction representation is subjected to feature extraction by adopting the segmentation aggregation method; and finally the result subjected to the feature extraction forms the classification method by adopting the Euclidean distance.

The present invention discloses a method and system for tax and financial supervision, which is based on original certificate data in transaction. The method consists of supervisor collecting original certificate data in transaction from supervisee by setting transaction data center, adopting the same data structure to store and process, and completing integral statistic and sorting analysis directly by making use of various information of supervisee contained in original certificate data in transaction. The system comprise data applied system, several transaction data sub centers, each of which connects with others bi-directional via network, and transaction terminals, which connect with access system of transaction data center via network or artificial process.

The invention discloses a multi-dimensional IRT-based teachers teaching ability evaluation method. The evaluation method comprises the following steps: a step of information collection before teachersteaching evaluation, a step of collecting the students evaluation of the teachers classroom language teaching ability which is needed for teachers ability evaluation, a step of preparation for the teachers teaching evaluation, a step of classification analysis on teachers teaching ability, and a step of summarizing the teachers teaching evaluation. The beneficial effects of the method are that via multi-dimensional IRT-based teachers teaching ability evaluation method, comprehensive evaluation of the teachers teaching ability can be realized, operation is simple, the teachers teaching abilityis fully explored, influence exerted by human factors can be prevented, veracity of teachers teaching ability evaluation can be ensured, the method can help reduce the evaluation of teachers teaching, multi-faceted evaluation is adopted, the accuracy of the evaluation of teachers teaching ability can be improved, and evaluation of competent teachers can be facilitated.

The invention provides a customer relation discovery method based on data vectorization spatial analysis, and mainly relates to the technical field of data processing and data mining. The customer relation discovery method based on data vectorization spatial analysis is mainly implemented through vectorization processing of customer attributes. The method has high universality. Through uniform data processing and geographic spatial analysis methods, the problem that in the prior art, a clustering analysis method and a classifying analysis method cause analysis difference under different data sets is effectively solved.

The invention discloses a method for selecting the function-maintenance testing points of an avionic assembly, which comprises the following steps of: carrying out classification analysis on an analog-circuit testing method, and carrying out the focused analysis of a fault dictionary method; establishing a preferred mathematical model of the testing points on the basis of a fuzzy set; selecting an optimal testing point, wherein the testing points are as few as possible; and establishing a preferred strategy of the testing points, making a preferred scheme of the testing points through the mathematical model and a fault-diagnosis analyzing method, and realizing the highest fault-detecting rate and the highest fault-isolating rate with the lowest testing cost. The invention provides a new preferred testing-point algorithm, and practical circuits are combined for simulation, so that the problem that the division of a traditional fuzzy set and the selection of the testing points are not optimized enough is solved, and the fault detecting and isolating efficiency of an analog-circuit system is improved.