344 results about "Applicability domain" patented technology

The invention discloses an advanced geological prediction method for underground engineering. With geology as pivot, the advanced geological prediction method for underground engineering comprises four stages of long-term advanced geological prediction, middle-term advanced geological prediction, short-term advanced geological prediction and impending advanced geological prediction. The advanced geological prediction method comprises the following steps of: (1) making advanced geological prediction tasks for underground engineering for the underground engineering clear; (2) collecting data; (3) carrying out field geological review; (4) perfecting the modification of a longitudinal section drawing of the underground engineering; and (5) adopting combined geological method, geophysical prospecting method and horizontal drilling method to obtain advanced geological prediction. The invention provides the advanced geological prediction method for underground engineering; with geology as pivot, comprehensive geological analysis is carried out in the whole process of advanced geological prediction for underground engineering and the long-term, middle-term, short-term and impending advanced geological predictions are closely combined together to implement combination of geological method, geophysical prospecting method and horizontal drilling method; the comprehensive application of the geophysical prospecting method is optimized; and the advanced geological prediction method for underground engineering, provided by the invention, has the advantages of improving the predication accuracy, reducing predication cost and obtaining wide application range, etc.

The invention discloses a method for fitting and interpolating a G01 code based on a quadratic B spline curve, comprising the following steps of: by an adaptive approach selecting each characteristic point of each group of small line segment which is described by the G01 code; fitting a route which is to be processed with the quadratic B spline curve of all the characteristic points; according to the characteristic of the quadratic B spline curve and the limit of the acceleration of each driving shaft of the numerical control machine, simultaneously obtaining the maximum permissible machining velocity curve (VLC curve) of the quadratic B spline curve and the each speed key point on the VLC curve; according to the each speed key point, the control axis of the each key point, the maximum permissible machining velocity and the VLC curve, computing real machining velocity; according the real machining velocity curve and a interpolating error computing interpolating point and completing real-time interpolation. The invention has fast computing velocity, high machining precision, stable working performance and wide application range, can complete the interpolating computation of the spline curve in real time and meet digital control processing requirement of fast velocity and high precision under a premise that the preset precision of the system is met.

The invention discloses a training method and a training device of a convolutional neural network (CNN) model, and belongs to the field of image recognition. The training method comprises the steps of respectively carrying out a convolution operation, a maximum pooling operation and a horizontal pooling operation on a training image so as to acquire a second feature image; determining a feature vector according to the second feature image; carrying out processing on the feature vector so as to acquire a category probability vector; calculating a category error according to the category probability vector and the initial category; adjusting model parameters based on the category error; and continuing the model parameter adjusting process based on the adjusted model parameters, and using model parameters at the moment when the number of iterations reaches a preset number of times as model parameters of the well trained CNN model. According to the invention, the convolution operation and the maximum pooling operation are carried out on the training image on different levels of convolution layers, and then the horizontal pooling operation is carried out. The horizontal pooling operation can extract a feature image marking a horizontal direction feature of the image from the feature image, so that the well trained CNN model is ensured to recognize images of any size, and the application range of the well trained CNN model in image recognition is expanded.

Novel tools and techniques for generating and/or implementing an applied semantic knowledgebase. Some tools allow for data integration into coherent, semantically connected networks and for generation of sets of query-based models describing complex functional relationships as sub-networks. In an aspect, an applied semantic knowledgebase may comprise collections of SPARQL network queries describing a specific set of sub-network relationships and their applicable ranges for each element in the query.

The invention relates to a method for realizing optimization of a data source extensive makeup language (XML) query system based on sub-queries. The method comprises the following steps: receiving corresponding XML query input information; carrying out morphological analysis and syntactic analysis, and verifying the correctness and the validity; if the analysis is successful and the verification is passed, generating an XML analytical syntactic tree; translating the XML analytical syntactic tree, and converting the XML query input information into intermediate logical representation; carrying out rewriting treatment, and generating a target query expression; and calling supported querying and computing engines for querying and computing, and acquiring output query results. By adopting the method for realizing the optimization of the data source XML query system based on the sub-queries, the query rewriting can be used for converting a procedural query into a descriptive query, the sub-queries are optimized, and certain specific sub-queries are rewritten into equivalent attended operation of a plurality of tables by the combination of the sub-queries, thus the levels of query sentences are reduced as much as possible, the treatment of planning optimization can be carried out conveniently, and the method has stable and reliable working performance and wider application range.

The invention discloses a fault diagnosis method for a rolling bearing, and belongs to the technical field of fault diagnosis and signal processing analysis. High-frequency sampling and preprocessing of vibration signals are firstly performed on the rolling bearing with faults, the preprocessed signals are sequentially filtered by the aid of a Morlet wavelet filter, spectral kurtosis and unit spectral kurtosis of the filtered signals are calculated, and a filter parameter corresponding to the maximum value of the unit spectral kurtosis is selected by comparison, namely, a global optimization filter parameter is selected. According to the fault diagnosis and detection method, an operator can extract the optimal filter parameter in the diagnosis process without a lot of detection experience and numerous historical data, the method is wider in application range, errors caused by human errors are greatly decreased, the extracted optimal filter parameter can be more accurate, a diagnosis result is more correct, fault diagnosis and detection automation is more facilitated, more time is saved, and efficiency is higher.

The invention discloses a runoff prediction method based on an attention mechanism and LSTM by using a deep learning algorithm. The method comprises the following steps: firstly, collecting influence characteristics related to runoff in a drainage basin, then constructing a time sequence data set corresponding to the characteristics and runoff, obtaining a runoff prediction model based on an attention mechanism and LSTM through training, and predicting the subsequent runoff according to the obtained runoff prediction model. Meanwhile, some short-term important features are ignored when the LSTM memorizes a long-term sequence mode, so that an attention mechanism is added, key elements in a runoff sequence are selectively concerned, the capability of capturing effective features by the LSTM is improved, and the prediction precision is relatively high. In addition, a deep learning method driven by data is used, dependence on a hydrological and physical mechanism in a drainage basin is reduced, and the application range of the model is effectively expanded.

The invention provides a side slope stable reliability sensitivity analysis method based on Monte Carlo simulation. The side slope stable reliability sensitivity analysis method based on the Monte Carlo simulation includes: step 1, constructing a joint probability density function of uncertain parameters; step 2, using a Monte Carlo simulation method to obtain a side slope failure probability, and obtaining a failure sample; step 3, designing various sensitivity analysis schemes, and respectively constructing joint probability density functions of uncertain parameters under all the sensitivity analysis schemes; step 4, obtaining side slope failure probabilities under all the sensitivity analysis schemes; step 5, obtaining a variation trend of the side slope failure probabilities along with statistical characteristics of the uncertain parameters according to the side slope failure probabilities under all the sensitivity analysis schemes. The side slope stable reliability sensitivity analysis method based on the Monte Carlo simulation is wide in application range, simple in computation process, high in computation efficiency, and capable of effectively revealing a response regularity between the reliability level of a side slope and the statistical characteristics of the uncertain parameters, and has certain guiding significance for side slope risk control, design optimization and the like.

A method and apparatus for training a model is disclosed in embodiment of the present application. One embodiment of the method includes obtaining a configuration file, wherein the configuration filecomprises a data set identification, a machine learning framework identification, a machine learning algorithm identification and parameter information; selecting data from the data set indicated by the data set identification as training data to generate a training data set; selecting the machine learning framework indicated by machine learning framework identification as the target machine learning framework from the preset set of machine learning frameworks; in the framework of target machine learning, using the machine learning algorithm to identify the indicated machine learning algorithmand the parameters indicated by the parameter information, and obtaining the classification model by training based on the training data set and the labels associated with the training data in the training data set. The embodiment enables users to select different machine learning frameworks, corresponding machine learning algorithms and parameters according to actual needs, and expands the application scope of model training.

The invention relates to a human body behavioral modeling identification method based on a priori knowledge cluster in a computer system. The method comprises the steps of human body behavioral modeling processing and human body behavioral identification processing. By the adoption of human body behavioral modeling identification method based on the priori knowledge cluster in the computer system, a constructed modeling system has a self-learning capacity and can continuously perfect a classifier along with the classification of observed samples. In cluster distribution obtained in the process of human body behavioral modeling, if outliers exist, the outliers serve as an abnormal behavioral model. When the system is designed, precaution is given as required, the method has great realistic meanings, important influence of priori knowledge on a human body behavioral identification result is considered, an improved cluster method is adopted to model and identify the human body, and foundation can be laid for identifying abnormal behaviors. The method is simple and efficient, can accurately identify different behavioral modes, and has good expandability. Working performance is reliable and stable, and the application range is wide.

The invention provides an on-line prediction method of ship navigation behavior, belonging to the ship navigation field. The method includes training learning stage and on-line prediction stage. The training learning stage sorts and divides the historical AIS big data first, and then trains and learns the intercepted data by using two-way long-short-time memory loop neural network. In the on-lineprediction stage, the AIS receiver is used to collect real-time AIS data, and then the sliding window algorithm is used to obtain the key feature points of ship trajectory, and then the future six consecutive ship trajectory points are predicted, and the final six consecutive future ship trajectory points are used as the predicted ship sailing behavior after multiple predictions. The mature modeltrained by big data has a wide application range and high versatility, which can be used in the later practical application to further enhance the prediction ability of the frequently predicted ships,and can be used in the ship intelligent collision avoidance decision-making and ship abnormal behavior detection, to provide services for the water traffic management department.

The invention relates to a method for automatically extracting character relations from a Chinese text or a text set, and belongs to the technical field of computer science and information extraction. In the method, by means of sentence meaning model characteristics, the relation attribute affiliation is determined; the character relations scattered in the text or the text set are automatically extracted by combining methods such as relation attribute disambiguation, character relation strength calculation and the like; and the character relations are organized by a character relation network, and the character relations comprising character relation attributes and the relation strength are displayed in a character relation graph manner. According to the method, sentence meaning model characteristics are introduced, so that the accuracy of the method for extracting entity relations is improved, and the method for extracting the character relations is enriched. Besides, as the number of texts about a central character in the text set is increased, the method can extract character relations of the central character more accurately and comprehensively, and the application range is wider.

The invention provides a recording device clustering method based on Gaussian mean super vectors and spectral clustering. The method comprises the steps that the Melch frequency cepstrum coefficient MFCC characteristic which characterizes the recording device characteristic is extracted from a speech sample; the MFCC characteristics of all speech samples are used as input, and a common background model UBM is trained through an expectation maximization EM algorithm; the MFCC characteristic of each speech sample is used as input, and UBM parameters are updated through a maximum posteriori probability MAP algorithm to acquire the Gaussian mixture model GMM of each speech sample; the mean vector of all Gaussian components of each GMM is spliced in turn to form a Gaussian mean super vector; a spectral clustering algorithm is used to cluster the Gaussian mean super vectors of all speech samples; the number of recording devices is estimated; and the speech samples of the same recording device are merged. According to the invention, the speech samples collected by the same recording device can be found out without knowing the prior knowledge of the type, the number and the like of the recording devices, and the application scope of the method is wide.

The invention belongs to the technical field experimental instruments, which relates to a multi-functional reaction device used for chemical engineering experiment. The multi-functional chemical engineering experiment device comprises a chemical reaction kettle, a control system and a feeding and discharging and metering system of reaction materials. The chemical reaction kettle adopts a double-layer high-transparent visible kettle body. An upper kettle cover and a lower kettle cover are made of stainless steel materials. The double-layer kettle body adopts conduction oil with high degree of transparency to heat, and the agitation is carried out by adopting strong magnetic force under static sealing. The control system comprises an in-kettle temperature measuring device, a kettle sleeve temperature measuring device, an in-kettle pressure measuring and transmitting device, a motor rotation speed measuring and transmitting device. The control system is connected with a control panel, andcan be connected with a computer to realize remote operation. The feeding and discharging and metering system of reaction materials comprises a feeding and metering system and a backflow separating and metering system of liquid and gaseous reaction materials. The multi-functional chemical engineering experiment device adopts a clasp or bolt connecting structure, wherein clasps and bolts are standardized and general and can be interchanged. The components can be added or cut voluntarily according to the change of the technique. The invention can be applied widely.

The embodiment of the invention provides a deep-learning artificial intelligence model establishment method and system. The method comprises: a deep-learning artificial intelligence model establishment request of a user is received and a model establishment job stream is generated based on the deep-learning artificial intelligence model establishment request; and according to the model establishment job stream, a deep-learning artificial intelligence model corresponding to the deep-learning artificial intelligence model establishment request is generated. According to the method and system provided by the invention, the deep-learning artificial intelligence model is generated automatically based on the deep-learning artificial intelligence model establishment request sent by the user and thus the user does not need to carry out software development, so that the user can carry out operation conveniently and the professional request of the user is reduced; and the application range is wide.

Provided is a pavement identification method for a reinforced road in an automobile proving ground. The method comprises steps of: acquiring the motion information of a vehicle body and axles by an acceleration sensor and an angular rate gyroscope, and acquiring CAN message data on a vehicle bus via an OBD interface of the vehicle; preprocessing acquired multi-source data to obtain stable time-domain data; computing the attitude information of the vehicle body and the axles and analyzing the protocol of the CAN messages; transforming the vibration acceleration data and attitude data from the time domain into the spatial domain by using vehicle speed; performing time-domain and frequency-domain feature extraction on the data in the spatial domain; designing a artificial neural network pavement classifier based on the extracted time-domain and frequency-domain features to identify the reinforced road in the automobile proving ground. The method solve a problem that an independent suspension is insensitive to landforms, compensates the defect of a conventional method that identifies the pavements just by using acceleration data, and greatly increases the identification accuracy and application range of the test road landform and ground.

A structure finite element model correcting method based on multi-element uncertainty comprises the following steps: (1) building an initialized parameterization equivalent finite element model in finite element software; (2) screening out significance parameters; (3) obtaining sample points, and constructing an incomplete variable high-order response surface model; (4) judging validity of the response surface model, if the validity of the response surface model meets the requirement, executing the next step, and if the validity of the response surface model does not meet the requirement, executing the step (3) again; (5) building a rapid random sampling analysis model with the combination of a high-order response surface and the Monte Carlo method, and conducting statistics on a mean value and a covariance matrix of simulation output responses; (6) conducting statistics on a mean value and a covariance matrix of test output results; (7) constructing a weighting objective function of the mean values and covariances of tests and simulation; (8) reversely estimating a mean value and a covariance matrix of input parameters; (9) judging whether the mean value and the covariance matrix of the input parameters meet correction accuracy or not, if yes, stopping iteration, and if not, executing the step (8) again. According to the structure finite element model correcting method, the calculated amount of the iteration is reduced, the application range is wide, and optimization of a large-scale parameter range is achieved.

The present invention provides an information providing method and apparatus, which have advantages of being objective, high in efficiency, wide in application scope, and good in scalability. The method comprises: extracting a sample feature vector and a corresponding sample decision from historical consultancy session data, wherein an element of the sample feature vector is an attribute value extracted from the historical consultancy session data according to a preset attribute, and the sample decision is a user questioning sentence; performing training by using a plurality of sample feature vectors and corresponding sample decisions to obtain a probabilistic classification model; extracting a to-be-tested feature vector from a current client consultancy request, wherein the to-be-tested feature vector is the same as the sample feature vector in format; inputting the to-be-tested feature vector into the probabilistic classification model, then receiving one or more candidate decisions and corresponding probabilities output from the probabilistic classification model, wherein the candidate decisions are candidate user questioning sentences; choosing K candidate decisions of greatest probabilities as forecasted decisions, then providing a client with a standard reply corresponding to the forecasted decisions.

The invention discloses a pharmaceutical activity prediction and selection method based on genetic expressions and drug targets. The method comprises following steps: 1) obtaining target genes corresponding to drugs to be detected according to information in drug target database; obtaining genetic expression data of disease tissues of patients and corresponding compression data and obtaining important genetic lists of morbidity processes of patients by evaluating importance of genes through a systems biology method; 2) searching for whether the target genes obtained in the step 1) target the important gene lists during mobility of the patients or not through statistical analysis of in order to predict pharmaceutical activity of patients and select drugs suited to patients. The method is easily used with high efficiency and broad application scope. The prediction method can be used for selecting drugs suitable for individual patients such that a personalized treatment schme can be provided for patients.

The invention relates to the field of micro-grid photovoltaic power generation system research, in particular to a micro-grid photovoltaic power generation short-term prediction method based on deep learning, which comprises the following steps of: 1, data preprocessing: performing supplementary correction on bad data and missing data in historical data, and then performing normalization processing to form training sample data; 2, dividing the processed sample data into sample subsets of different seasons and different generalized weather types, and inputting the sample subsets into an LSTM prediction model as training samples; 3, establishing an LSTM short-term prediction model according to the weather type, and training is completed; And 4, selecting a corresponding LSTM prediction sub-model according to the season to which the prediction day belongs and the weather type prediction information, and outputting a photovoltaic power generation short-term prediction value at a future moment. According to the method, the deep learning algorithm is adopted to deeply mine the output characteristics of the photovoltaic power generation and establish the prediction model, expensive meteorological measurement equipment and instruments are not needed, the cost is reduced, and the method has a wide application range.

The invention discloses an industrial process monitoring method based on a missing variable PCA model, so as to use a missing variable processing method to online estimate principal component information and realize the purpose of executing monitoring on estimation errors. Firstly, the principal component is estimated by assuming that each measurement variable data is missing one by one; and then, with the estimation error of the principal component and the error estimation value of the PCA model as monitored objects, online process monitoring is executed. Although sampling data under a normal working condition do not necessarily satisfy the Gauss distribution hypothesis, the estimation errors generally obey the Gauss distribution. From the point of view, although the method is based on the PCA algorithm, the method does not require hypothetical training data to obey or approximately obey the Gauss distribution, and the application range of the traditional PCA-based process monitoring method is extended to a certain extent. Besides, the method also has the advantage of a high multi-model generalization capability as multiple fault detection models are adopted.