88 results about "Temporal models" patented technology

A system and method for dialog state tracking employ an ontology in which a set of values are indexed by slot. A segment of a dialog is processed to detect mentions. Candidate slot values are extracted from the ontology, based on the detected mentions. The candidate slot values are ranked. A dialog state is updated, based on the ranking of the candidate slot values, which may be conditioned on the output of a temporal model, which predicts whether the value of the slot has been instantiated, modified, or is unchanged.

The invention provides an SPH (smoothed particle hydrodynamics) algorithm-based simulation method and an SPH (smoothed particle hydrodynamics) algorithm-based simulation system of the process of breaking dam by flood. The simulation method comprises the following steps: A. acquiring on-site geographic spatial information data; B. establishing a dynamical model of the process of breaking dam by flood based on the geographic spatial information data obtained in the step A; C. establishing a geographic entity model according to the geographic spatial information data obtained in the step A; D. analyzing the dynamical model obtained in the step B into an SPH calculation method; E. initializing the geographic entity model obtained in the step C to be hydrodynamics particles and boundary particles for the SPH calculation; F. circularly calculating based on the SPH algorithm; G. carrying out the spatial-temporal process modeling on the calculated value result obtained in the step F to obtain a 3D spatial-temporal model and a database of the process of breaking the dam by flood; and H. dynamically visualizing the 3D spatial-temporal model and the database of the process of breaking dam by flood obtained in the step G. By using the SPH method in the geographic process simulation, the authenticity of the simulation result is effectively improved.

A system and method for processing acquired images to develop useful classifications of subjects such as occupants of a vehicle preferably employs a hierarchical and probabilistic structure, such as a Bayesian Network to analyze acquired images and produce a meaningful classification. The structure preferably includes set of analyzers, a set of Scenario analyzers and a set of Temporal models which are arranged in three respective hierarchical layers. Each respective analyzer operates on the acquired image and, in some circumstances, feedback from the Scenario analyzers, to produce an output representing the probability that a feature that the respective analyzer is concerned with is present in the acquired image. Each respective Scenario analyzer receives output probabilities from at least one of the analyzers and, in some circumstances, feedback from the Temporal Models, to produce an output indicating the probability that a scenario that the respective Scenario analyzer is concerned with, is the scenario captured in the acquired image. Each respective Scenario analyzer can also provide feedback inputs to one or more analyzers to alter their operation. Finally, each respective Temporal Model receives and operates on the output from at least one Scenario analyzer to produce a probability that a classification with which the Temporal Model is concerned is represented by the acquired image. Each respective Temporal Model can also provide feedback inputs to one or more Scenario analyzers to alter their operation. The structure processes the classification probabilities output from the Temporal Models to produce a classification for the acquired image.

Gaps in proficiencies may be identified within an enterprise. Understanding gaps in the existing workforce may help inform training, hiring, and firing decisions to ensure successful completion of the upcoming projects and deadlines. Using a multi-level model for each proficiency that accounts for enterprise needs as well as hiring, retraining, and the like, a relationship between proficiencies, projects, and employees over time may be generated as a multi-dimensional temporal model. The temporal model may be simulated to forecast gaps in proficiencies of the employed workforce. Recommendations regarding retraining, hiring, and termination can be made to help users remedy the deficiencies. Additionally, the proficiencies most valuable to the enterprise may be determined using a catalog of proficiencies to cluster the proficiencies into proficiency clusters for each job or job category and the proficiencies scored. Employees and candidates may be scored using the clusters to inform hiring, firing, and retraining decisions.

The invention discloses a method, a system, a computer device and a storage medium for establishing a composition model. The method comprises the following steps: obtaining a music data set of a MIDIformat and converting the music data set of the MIDI format into a piano key axis; and converting the music data set of the MIDI format into a piano key axis; cleaning up the data of piano key axis after format conversion; establishing the interference track model and the composing track model by using the generated countermeasure network, and establishing the mixed track model by combining the interference track model and the composing track model; dividing the generator into a time structure generator Gtemp and a music bar generator Gbar, and constructing the temporal correlation between themusic bars to form a temporal model by the time structure generator Gtemp and the music bar generator Gbar; combining the hybrid track model and the time model to form a multi-track symphony composition model. The above composing method satisfies people's requirements for diversification of musical changes and is natural.

The invention discloses a method and system for unified management of AI models based on a distributed file system. Based on the distributed file system, a model iteration management module is additionally arranged to extract preset model file information; wherein the model file information comprises information such as a model name, a model version, model creation time, whether a model is onlinepublic and whether the model is dirty, an AI model record is newly added in the metadata table, and the model is stored in a model warehouse according to a preset model storage path to construct an AImodel management system formed by combining the metadata table and the model warehouse. The newly-added model reading module analyzes data input by a user, extracts model information, matches the model information with records in a metadata table, extracts metadata items in the table, checks whether a model is online or not, and if the model is online, extracts complete target model information including a dirty model state from nodes of the distributed file system according to metadata and returns the complete target model information to the user. A user can use the model in real time and can also optimize the model and upload the model again to facilitate optimization and updating of the model.

According to the present invention, a method for a travel time predicting apparatus to predict a travel time by using a travel time prediction model, including generating a first prediction model for prediction of a travel time of a vehicle that will pass through a specific area by using first traffic data that are related to a travel speed at the specific area; generating a second prediction model by modifying the first prediction model that relates to the travel speed at the specific area and is different from the first traffic data; and predicting a travel time of the vehicle that will pass through the specific area by using the second prediction model can be provided so that a travel time at which a specific vehicle passes a specific area can be more precisely predicted compared to a conventional art and predicted travel time information can be promptly and precisely provided to the user.

The invention discloses an industrial robot stop motion trajectory planning method. According to the method, stop planning is carried out directly for a joint position finally output in each interpolation period, delay from a trajectory interpolation layer to a joint position output layer is avoided, and response is rapider and more timely. Discrete interpolation output joint positions are storedand added with time information at the same time, a cubic spline model is used for converting discrete joint position information into continuous information in a time domain, then a time scaling method is used for stopping planning, it is guaranteed that data output in each period in the stopping process still come from the joint position output through trajectory interpolation, and then it is guaranteed that there is no trajectory deviation in the stopping process. Meanwhile, a simplified quintic polynomial is adopted to plan time, a model is simple, the calculated amount is small, and continuous, smooth and impact-free operation of the whole stop process is guaranteed through the high-order trajectory planning method.