61 results about "State representation" patented technology

A system and method are disclosed for recreating graphics processing unit (GPU) state information associated with a migrated virtual machine (VM). A VM running on a first VM host coupled to a first graphics device, comprising a first GPU, is migrated to a second VM host coupled to a second graphics device, in turn comprising a second GPU. A context module coupled to the first GPU reads its GPU state information in its native GPU state representation format and then converts the GPU state information into an intermediary GPU state representation format. The GPU state information is conveyed in the intermediary GPU state representation format to the second VM host, where it is received by a context module coupled to the second GPU. The context module converts the GPU state information related to the first GPU from the intermediary GPU state representation format to the native GPU state representation format of the second GPU. Once converted, the GPU state information of the first GPU is restored to the second GPU in its native GPU state representation format.

The invention provides a state self-checking method of a combined speed measuring and positioning system for a train, which comprises the following steps: judging the state of a speed sensor by using the respective multi-path information of the speed sensor for carrying out the redundant check; judging the measuring state of the speed sensor by taking radar measuring data as reference; judging the measuring state of a radar sensor according to the measuring data of the speed sensor and the output state word of a radar; and determining a system structure reconstruction scheme based on a system state transfer model according to the state judging results of the speed sensor and the radar, outputting speed measuring and positioning information which does not contain invalid or fault sensor or radar measuring results, and giving system state information representation or giving state representation of invalid measuring output when all sensors are invalid. The method of the invention improves the state detection performance of the sensors, reduces the complexity of the combined system, and can further save the engineering expenses and the maintenance cost.

The invention puts forward a plug-in hybrid electric vehicle energy management method based on deep reinforcement learning. The method comprises the following steps that: carrying out representation extraction on multisource high-dimension driving working condition information, and processing the high-dimension driving working condition information into a low-dimension representation vector; utilizing a redundant information rejection algorithm to carry out dimensionality reduction and fusion processing on obtained working condition state representation, including a low-dimensional representation vector, the own state information of a vehicle, a gradient and the like, to obtain low-dimensional continuous working condition information; constructing a plug-in hybrid electric vehicle energy management framework based on the deep reinforcement learning, and inputting the low-dimensional continuous working condition information to finish offline training; and utilizing a trained strategy tocontrol energy distribution, providing an approach for comprehensively considering the influence of multisource high-dimensional driving working condition information on a plug-in hybrid electric vehicle energy management effect, and utilizing a reinforcement learning autonomic learning optimal energy distribution scheme to mine the energy saving potential.

The invention discloses a rolling bearing fault diagnosing method and equipment based on CEEMDAN and CFSFDP, belonging to the field of fault diagnosis of rotary machines. The method comprises the steps of acquiring vibration signals of a bearing in a normal state and different fault mode states so as to obtain sample points of the vibration signals of different states, decomposing by using CEEMDANto obtain time and frequency domains characteristics of bearing diagnosis and screening out bearing state representation parameters along with the time domain and frequency domain characteristics, dividing the representation parameters into training samples and a test samples, then using a table CFSFDP algorithm as a bearing fault diagnosis model, inputting the training samples into the bearing fault diagnosis model, clustering output results, obtaining clustering amount, clustering center points of each type, and state types corresponding to the clustering center points, and inspecting the trained diagnosis model by using test samples. The method and equipment can identify different bearing fault types and fault degrees accurately and effectively.

The invention provides ultrasonic 3D vibration and ELID (Electrolytic In-process Dressing) combined precision grinding system and method. The grinding system comprises a multi-channel ultrasonic generator, an ultrasonic objective table, an automatic distance adjusting electrode, a high frequency DC pulse power supply and a computer monitoring system; the computer monitoring system is used for synchronously acquiring ELID and 3D ultrasonic vibration state representation signal, observing the working state and mutual influence degree of each combination process, and then automatically or manually performing feedback control, so as to enable the two processes to be dynamically harmoniously and optimally matched; when in grinding and processing, a grinding wheel contacts with the grinding surface of a workpiece to cause an electrolyte ultrasonic cavitation phenomenon which can effectively prompt the actual grinding effect of the grinding wheel, and the grinding force is stable. According to the system and method, the ultrasonic grinding processing technology and the ELID grinding technology are combined and supplement each other in advantages, so as the overcome the defects of single process; the technical support is provided for the application of the efficient processing of ultra-smooth surface of a hard and brittle material in the high-tech fields such as national defense and aerospace.

A method and apparatus for depicting programming state of programming objects through a combination of associating semantic stat space with programming objects, updating semantic state through affiliated state updates with each programming action, and resolving the alternative programming states with a convergence operator defined on the semantic state space of the programming object type. The state reflection process is further defined in cases where the visual programming object participates in multiple execution paths, as would be the case in, for example, an IF statement wherein the variable is used on both the Left and Right sides. More particularly, this technique resolves these differing states back into a single state representation for the programming object.

A method for accepting a session in an information system server includes generating a representation of the session. The representation includes a first plurality of parameters that define a proposed additional load of the session on the information system server. A determination is made of a current state representation of the information system server. The current state representation is defined by a second plurality of parameters. The current state representation defines a current load on the information system server at a time instant. A determination is made of a headroom representation for the current state of the information system. The headroom representation is defined by a distance between a model surface.

The invention provides a reinforcement learning based automatic picture cutting method and device, relates to the field of image processing, and aims at obtaining an accurate picture cutting result rapidly. The method comprises the steps that a reinforcement learning model is used to carry out feature extraction on a present cutting window to obtain a local feature, the local feature is spliced toa global feature of the picture to be cut to obtain a new characteristic vector, and the new characteristic vector serves as present observation information; historical observation information obtained via the reinforcement learning model and the present observation information are combined and serve as present state representation; and according to a cutting strategy and the present state representation, a cutting motion is carried out on the picture to be cut sequentially, and a cutting result is obtained. The reinforcement learning model is a model which is established on the basis of a convolutional neural network. The method and device can be used to obtain the accurate picture cutting result rapidly.

The invention discloses an urban region road network running state characteristic information extraction method. The method comprises steps that, step 1, the road network data is acquired; step 2, a road network data input matrix is constructed; step 3, adaptive neighborhood selection is carried out; step 4, characteristic information extraction is carried out; and step 5, visual representation of the road network running state characteristic information is carried out. The method is suitable for urban region road network characteristic information extraction and road network running state representation, on the basis of road network flow, speed and density data, an adaptive neighborhood selection manifold learning method is utilized to extract the road network running characteristic information, the road network running characteristic information has macroscopic property, accuracy, sensitivity and practicality, a macroscopic road network running state can be timely represented, and analysis and decision bases are provided for traffic managers.

The presently disclosed inventive concepts relate to establishing a state representation of a web page represented in a web browser. This concept includes processing a web page retrieved from a web based data source; establishing a resulting first internal browser state representation of the web page; establishing an external representation representing a state of the first internal representation; establishing a representation of a first content of the first internal browser state representation; establishing a dynamic content state representation which represents the state of the dynamic content of the first internal browser state representation; establishing mapping data comprising relationships between the dynamic content and the first content; and making the external representation available to a web browser application so as to establish a further internal representation of the web page in the web browser application at a state corresponding to the state of the first internal browser state representation.

The invention relates to a ship monitoring method, device and system, and a storage medium. The method comprises the following steps: acquiring state information of ship-borne equipment sent by acquisition equipment, wherein the acquisition equipment is used for receiving source information of ship-borne equipment acquired by a plurality of sensors, and the state information is obtained from the source information; determining a data processing mode corresponding to the acquisition equipment; performing data processing on the state information of the acquisition equipment by adopting the dataprocessing mode to obtain a state representation value of the ship-borne equipment; obtaining a state representation value of the ship according to the state representation value of the ship-borne equipment; and comparing the state representation value of the ship with a preset alarm threshold value, and sending prompt information to a monitoring center corresponding to the ship according to a comparison result. By adopting the method, the influence of external environment and subjective factors can be avoided, and the monitoring result is accurate; A real-time monitoring of the working stateof a ship is realized, the timely maintenance of the ship is facilitated, and the efficiency is high.

The invention discloses an optical quantum OFDM communication system and a quantum information detection method thereof. The invention introduces the orthogonal frequency division multiplexing technology to the quantum communication, and provides the quantum communication system based on an all-optical OFDM model; meanwhile, the invention presents that a transmitted quantum state symbol set is expanded under the optical quantum digital state representation, and the minimum square root measurement (SRM) method with better performance in the positive operator valued measure (POVM) is used for algorithm measurement. The invention gives the system design and the basic principle explanation and performance simulation method and result, and performs simulation on the quantum state detection performance and part factors affecting the system performance separately. Results show that for the optical quantum OFDM system, the bit error rate performance of the quantum detection method based on theminimum square root is better than that of the classical detection method.

An automated emotional recognition system includes an emotional state classifier adapted to receive, during an operative phase, an input information stream with embedded information related to emotional states of a person, and to generate a succession of emotional state indications derived from the input information stream. The emotional recognition system further includes a post-processing function, configured to receive at least two emotional state indications of the succession and, for each of said at least two emotional state indications, determine a corresponding emotional state representation in an emotional state representation system. The post-processing function is further configured to combine the emotional state representations of the at least two emotional state indications to obtain an output emotional state indication.

The invention discloses a state space-based multi-featured device state evaluation method and application, belonging to the technical field of device state monitoring. The method comprises the following steps: a marker post state comment set building link provides an evaluation output range; an optimized selection and standardization link for feature parameters provides standardization input; a state space building link provides a state representation mode; a space affiliation calculating link provides state evaluation indexes; and a state matching link provides the final output conclusion. In the invention, the research thinking on multi-featured device state evaluation is expanded, the traditional linear distortion phenomenon caused by direct linear superposition of multi-featured utility values can be solved, the formulation of state grading standard is enriched, contribution degree of all feature elements to the state evaluation result can be quantized, a scientific method for improving the state monitoring and analysis level of complex equipment is improved, labor cost is reduced and enterprise benefit is improved.

Systems and methods for optimizing a Lifetime Value (LTV) of a player of a plurality of computer-implemented games are disclosed. Data is collected from a game of the plurality of games, the data including game event data associated with the player, a playing environment within the game, and store action data. The data is analyzed with a first machine-learning (ML) system to create a time-dependent state representation of the game, the player, and the playing environment. The state representation is provided as input to a second ML system to create and optimize an ML policy over time, the ML policy including a functional relationship proposing a selection of one or more store actions within a store to maximize the LTV. One or more of the store actions chosen from the proposed selection in accordance with the ML policy and implemented within the store environment.

The invention discloses an incoherent soft-output detection method based on continuous phase modulation system, and mainly overcomes the defects that the existing incoherent detection is high in complexity and insufficient in detection performance. The method is implemented through the following steps: (1) defining state representations in a grid chart; (2) setting a moment before receiving as a moment receiving the zeroth symbol, and carrying out forward initialization on all states at the current moment; (3) sequentially carrying out forward updating from a moment receiving the first symbol to a moment receiving the Nth symbol in an ascending mode; (4) after the forward updating is completed, carrying out backward initialization on all states at the moment receiving the Nth symbol; (5) sequentially carrying out backward updating from a moment receiving the (N-1)th symbol to a moment receiving the zeroth symbol in a descending mode; and (6) after the forward updating and the backward updating are completed, merging prior information of a forward metric, a backward metric, a branch metric and input symbols, and outputting soft information. The method disclosed by the invention can be applied to the detection of an uncoded system and the simultaneous iterative detection in a serial concatenated system.

The invention provides a multi-agent cooperative control system and method for the process industry. A neutral network model from state values to observation values is arranged for feature extraction,important features of data are better extracted, and processing of mass data or even high-dimensional data can be adapted to. Then the minimax deep deterministic policy gradient algorithm is adoptedfor learning, wherein actor and critic networks in the minimax deep deterministic policy gradient algorithm conduct learning from a new neural network instead of conducting learning from initial observation. By means of the method for conducting state representation learning by utilizing the neutral network, the network can well capture features, and the adaptability to data is higher.

A signal transmission system for communicating across galvanic isolation. The signal transmission system includes first circuitry referenced 108 to a first potential, the first circuitry comprising signal transmission circuitry, second circuitry 110 referenced to a second potential and galvanically isolated from the first circuitry, the second circuitry comprising signal reception circuitry, and a magnetic coupling 112 between the first circuitry to the second circuitry across the galvanic isolation, the magnetic coupling comprising a transmitter-side inductor and a receiver-side inductor. The signal transmission circuitry can include a source coupled to output, to the transmitter-side inductor of the magnetic coupling, a first state representation that represents a first logic state with multiple transitions, the first state representation including at least a first upward transition, a first downward transition, a second upward transition, and a second downward transition.

This invention relates to a maximization of the inferences which are derived from data involved in data processing representing asset modes based on applicable state representations. The maximization takes place using a single algorithm to automatically analyze information and infer from electric power turbine configurations.

The invention discloses a diagnosis prediction method of a bidirectional recurrent neural network based on attention, and relates to the technical field of prediction diagnosis. The diagnosis prediction method firstly embeds high-dimensional medical codes (ie, a clinical variable) into a low code layer space, and then inputs code representation into a bidirectional recurrent neural network based on attention to generate hidden state representation. The diagnosis prediction method inputs the hidden representation through a softmax layer so as to predict medical codes accessed in future. The experimental data shows that, by using the diagnosis prediction method of a bidirectional recurrent neural network based on attention, when predicting future access information, the attention mechanism can assign different weights to previous accesses, thus effectively completing the diagnosis and prediction task, and reasonably explaining the prediction result.

The invention relates to an evaluation method for early damage of an additive manufacturing part based on metal magnetic memory detection and belongs to the field of metal magnetic memory detection innondestructive detection, wherein a mechanics theory and a force-magnetic coupling theory of ferromagnetic materials are used for guiding, and the variation condition of a residual magnetic inductionintensity standard deviation and a residual magnetic induction intensity gradient maximum value thereof along with the tensile load is used as an early damage state representation method of the additive manufacturing part. The residual magnetic induction intensity of the additive manufacturing part under different stretching states is detected by adopting a metal magnetic memory method, the earlydamage change process of the defect is characterized by the change of magnetic memory signals, and the residual magnetic induction intensity standard deviation and the residual magnetic field intensity gradient maximum value are used for representing the early damage situation of the additive manufacturing part. According to the method in the invention, the metal magnetic memory detection technology is combined with damage mechanics, the magneto-mechanical effect of the ferromagnetic additive manufacturing part is used to achieve nondestructive detection of the early damage of the ferromagnetic additive manufacturing part under the condition of not requiring magnetizing, and the judgment accuracy is high.