333 results about "Reference modeling" patented technology

A 3-D wireframe model expressed in terms of nodes and vertices receives a 3-D video or like signal representative of a scene expressed in terms of a reference model, I frames and P frames. A DPCM coder takes advantage of the temporal correlation of the displacement of each vertex along every axis in the 3-D space. The 3-D signal is a set of non-zero displacements of all vertices and all nodes (s_l[n, v]) at time t_l. The decoded set (animation frame) of the previous instance is used as the predicted value (s_i-l[n, v]). The prediction error e_l[n, v], i.e. the difference between the current displacement set and the predicted one, is computed and quantised (e_l[n, v]). Finally, the quantised samples are entropy coded (c_i[n, v]) using an adaptive arithmetic coding algorithm to handle the unknown data statistics. The predictive scheme described above prevents quantization error accumulation. A DPCM decoder first decodes arithmetically the received samples (‘e’ [n, v]) and computers the decoded samples (s_i′ [n, v]).

The invention discloses an SCADA (supervisory control and data acquisition) temperature parameter based wind turbine set security evaluation method and belongs to the technical field of equipment security evaluation. The wind turbine set security evaluation method includes: firstly, deciding on an SCADA temperature parameter; then analyzing temperature parameters related to system faults of a wind turbine set, structuring relatedness between operating conditions of the environment temperature as well as wind speed and the SCADA temperature parameter, determining a standard for division of wind turbine set operating conditions, and establishing a standard Gaussian model serving as a reference model for evaluation of the wind turbine set operating conditions; calculating degree of deviation between a curve and the standard model through comparing the standard model with the Gaussian model, and measuring the operating condition of the wind turbine set. By the application of the wind turbine set security evaluation method, monitoring level upon the wind turbine set can be improved, utilization rate of the wind turbine set can be increased, and operation cost can be effectively lowered; a reliable and effective wind turbine set security evaluation mechanism is established, so that deterioration of hidden trouble of the wind turbine set towards faults and accidents is avoided, and an operation circumstance which is secure, stable and efficient is formed for the wind turbine set.

A method for adjusting/monitoring an orthodontic appliance worn by a patient. Creating a numerical three-dimensional reference model of at least a part of a patient's arch. Creating a numerical three-dimensional reference model of the teeth of the at least a part of the patient's arch. Acquiring at least one updated image, under actual acquisition conditions, and searching, by deformation of the initial reference model, for an updated reference model corresponding to the positioning of the teeth during the acquisition of the updated image. Determining, depending a comparison model corresponding to an anticipated positioning of the teeth, a comparison model. Comparing the updated reference model with the comparison model. If comparison leads to an unsatisfactory result, creation of a replacement orthodontic appliance, by modification of the orthodontic appliance worn or by creation of a new orthodontic appliance, the replacement orthodontic appliance being configured accordingly.

A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.

The present invention relates to a universal sensor self-diagnosis device and to a diagnosis method therefor. In more detail, the device of the present invention includes: a sensor signal input unit for receiving a sensor signal from at least one sensor; an input data processing unit for processing the received sensor signal from the sensor signal input unit as a value available for a diagnosis device; a modeling unit for calculating a diagnosis modeling value by modeling data processed by the input data processing unit; a memory for storing a reference modeling value for the sensor signal of the sensor, which is modeled in advance when the sensor is in a normal state, and a failure type determining unit for determining a failure type of the sensor; a failure diagnosis unit for diagnosing a failure of a corresponding sensor by assessing the degree of correlation between the diagnosis modeling value and a reference modeling value of the corresponding sensor stored in the memory; and a failure type determining unit for determining a failure type of the corresponding sensor by assessing the degree of correlation between the diagnosis modeling value and the reference modeling value using the failure type determining data if it is determined by the failure diagnosis unit that there is a failure in the corresponding sensor.

The invention discloses a control method and device for a tilt rotorcraft. The control method comprises steps of: establishing a dynamics reference model of the tilt rotorcraft; according to the reference model, designing a control system for controlling the tilt rotorcraft; according to the reference model, determining the system state of the tilt rotorcraft and a model difference between the reference model and an actual model; operating the control system according to the system state to obtain an virtual control input; correcting the virtual control input according to the model difference to obtain an actual control input; controlling the flight of the tilt rotorcraft according to the actual control input. Thus, the corrected actual control input is used for controlling the motion of the tilt rotorcraft so that the flight of the tilt rotorcraft is stable.

The invention belongs to the technical field of car-following control and particularly relates to establishment of car-following system reference models and the adaptive optimization problem based on division of following states of car distance and speed and behaviors of behind-cars under the specific following states. The adaptive cruise control method is implemented by steps including: 1, establishing a car-following system model; 2, establishing the reference model of car-following control; 3, calculating dynamic safe car distance in real time; 4, according to the current specific car-following state, selecting a corresponding behind-car behavior modification mathematical model and calculating the control law of the car-following system in real time; 5, implementing the control law and achieving behind-car behavior modification; 6, detecting the following state of the following system in real time during modification of behind-car behaviors, turning to the step 4, and executing circularly. During the course that a behind car follows a front car, the control law for modifying self-behaviors is calculated in real time according to the current following state and car performance, behavior and vehicle distance information, so that the following operation which is safe, efficient and stable (comfortable) can be realized.

The invention relates to the technical field of computers, and provides a method and device for training an information extraction model and obtaining a knowledge graph and equipment for training theinformation extraction model and obtaining the knowledge graph, which are used for improving the robustness of the information extraction model. The method comprises: training an initial first information extraction model, and updating the first information extraction model based on a prediction result of a noise sample to obtain a first intermediate state model; updating the first information extraction model based on the difference between the prediction result of the noise sample by the first intermediate state model and the prediction result of the non-noise sample by the initial second information extraction model to obtain a second intermediate state model; updating the second intermediate state model based on the prediction result of the non-noise sample by the second intermediate state model to obtain a reference model; and adjusting parameters of the reference model based on a preset smooth coefficient to obtain a target information extraction model. The model parameters are updated based on the meta-learning mode, the updated model is more robust, and the constructed knowledge graph is more accurate.

The embodiment of the invention discloses a chip circuit function verification system, a method, equipment and a storage medium. The system comprises an excitation generation module, an execution module, a to-be-tested circuit, a reference model and a tester, the excitation generation module obtains a chip software simulation result, generates target verification excitation and inputs the target verification excitation to the execution module and the reference model; the execution module performs parameter configuration on the to-be-tested circuit according to the target verification excitation, generates a target excitation signal and inputs the target excitation signal to the to-be-tested circuit; the to-be-tested circuit generates a test output result according to the parameter configuration result and the target excitation signal; the reference model generates a reference output result according to the target verification excitation simulation; and the tester matches the test output result with the reference output result to obtain a function verification result of the circuit to be tested. According to the system of the invention, the time efficiency, the resource utilization rate and the accuracy of chip circuit function verification can be improved, and the realization of large-scale application scene verification is promoted.

The embodiment of the invention discloses an adversarial image generation method, device and equipment and a readable storage medium, and the method comprises the steps: generating a reference model equivalent to the classification of a target classification model according to the target classification model; obtaining a target image, and generating original noise for the target image according tothe reference model; inputting the first noise and the original noise into an adversarial model, and outputting second noise corresponding to the first noise when the adversarial model meets a convergence condition, wherein the second noise is the noise for enhancing the original noise information entropy; generating an enhanced noise image corresponding to the target image according to the second noise and the target image, wherein the classification accuracy of the enhanced noise image in the target classification model is smaller than that of the target image in the target classification model. By adopting the invention, the enhanced noise picture is difficult to identify by the malicious target classification model, and the security of the enhanced noise picture is improved.

The invention provides a method for training a cross-modal retrieval model, a cross-modal retrieval method and related devices, relating to the field of artificial intelligence. The method comprises the following steps: determining a reference model by utilizing unsupervised learning; performing knowledge distillation based on the reference model and the training data to obtain similar data of thetraining data; carrying out supervised learning by utilizing the similar data of the training data and the training data, so that a cross-modal retrieval model is obtained. According to the method and the device, relatively high accuracy of the trained cross-modal retrieval model can be ensured under the condition that the label of the training data used for supervised learning does not need to be manually labeled.

The invention provides a heavy carrier rocket attitude control method with customizable control performance indexes. On the basis of an attitude dynamics model of an aircraft, a reference model with an expected overshoot amount and rise time is designed; a is obtained and thus a model tracking error dynamics model is obtained and a model tracking error constraint function is designed, so that convergence from an initial state to an expected steady-state error within specified time is realized based on a certain change rule. External disturbance is estimated by using an adaptive disturbance estimation law based on the steps and an attitude controller is compensated; and with the controller, the convergence characteristic of fixed time can be ensured and the high control precision and robustness are realized. According to the provided method, a controller conforming to control performance indexes like an overshoot, rise time, a steady-state error and transition process time specified bya user can be designed according to requirements; and the consistent control performance of the aircraft in the whole flight process can be realized. The method has wide application space and great application value.

System, method and computer program products for failure analysis implementing automated comparison of multiple reference models. An exemplary embodiment includes a method for failure analysis for an instruction set implementation in a computer system, the method including running a test-case in a first and a second model, determining if the test case failed in the first model and determining if the test case failed in the second model.

The invention discloses an automobile transverse and longitudinal stability cooperative control method under an extreme working condition. The method comprises the steps: obtaining a four-wheel-hub-motor-driven electric automobile model through simulation software CarSim; designing a two-degree-of-freedom reference model, and deriving expected values of a lateral speed and a yaw velocity of the automobile through a two-degree-of-freedom reference model; adopting a double-layer control structure to reduce the solving complexity, employing an NMPC controller for the upper layer to ensure that transverse and longitudinal stability of the automobile is used as a control target, and carrying out optimized solution by considering transverse and longitudinal safety constraints to obtain virtual control variables, namely expected values of a tire slip rate and a slip angle; and finally, acquiring an additional torque acting on the hub motor according to the deviation between the actual slip rate and slip angle of the tire and the expected value given by the upper layer. Therefore, the transverse and longitudinal stability of the vehicle is ensured.

The embodiment of the invention provides an MCU verification method and system and terminal equipment. The method comprises the steps: generating a corresponding register configuration message according to the test excitation of a target register, and writing an address index and a pre-configuration value contained in the register configuration message into a storage unit of an MCU, so as to enable the MCU to operate the target register in a corresponding module; meanwhile, sending the configuration message to a reference model corresponding to the MCU, and varifying the system function of the MCU and/or the function of the corresponding module by comparing the expected response output by the reference model with the actual response output by the MCU. According to the method, most functions are put down to the module level for verification, only one unified module level simulation platform needs to be built; compared with an existing verification method, it is avoided that a large number of instruction programs need to be developed, and it is also avoided that many verification platforms need to be built for specific modules; according to the scheme, not only can the MCU be effectively verified, but also the utilization rate of a universal verification platform and the like are improved.

The present invention discloses a CT gray-material property assignment finite element modeling method used for osteotomy. The method comprises: (1) skeleton three-dimensional model reconstruction; (2) hook surface construction and substantialization of a skeleton surface model; (3) skeleton segmentation of a bone entity model; (4) meshing and output of a skeleton member entity model that the position of which is not changed; (5) position adjustment of a skeleton member entity model that the position of which is changed and establishment of a reference model; (6) CT image gray-material property experience formula based mesh model assignment; and (7) position adjustment of a skeleton member mesh model and processing before finite element modeling, and obtaining a final skeleton finite element model. The method disclosed by the present invention can realize random adjustment of the position of the skeleton member model, has gray assignment based non-uniform element material properties, and has characteristics of high reducibility of a model material, simple operations, an accurate finite element calculation result, and the like.

The invention discloses a device operation neural network test method and a device. In the embodiment of the invention, the method for testing the operation of the neural network by the to-be-tested device can comprise the steps: generating a reference model of the neural network through the description information of the neural network; calling a simulator to process a predetermined image according to the reference model of the neural network to obtain an expected result of the neural network; operating the neural network through a to-be-tested device to process the predetermined image so asto obtain a real result of the neural network; and determining whether the to-be-tested device can normally operate the neural network or not according to the real result of the neural network and theexpected result of the neural network. According to the method and the device, the test for the device operation neural network condition can be automatically completed without manually constructingtest data and preparing an expected calculation result, so that the labor load can be remarkably reduced, the test efficiency is improved, and the test accuracy is higher.

The invention relates to a model reference self-adaptive system parameter self-tuning method based on one-dimensional fuzzy control. The method comprises the following steps that (1) a model reference self-adaptive system based on the one-dimensional fuzzy control is structured according to an actual physical model of a permanent magnet synchronous motor; (2) direct-axis currents and quadrature-axis currents are calculated respectively according to the actual model and a reference model, and a current error variable e(t) of the direct-axis currents and the quadrature-axis currents is obtained according to an error calculation formula; (3) the changing range of e(t) is defined as current error variable pulsation band e(t)band, and the e(t)band is sent into a one-dimensional fuzzy controller after action of the input scale factor Ke of the one-dimensional fuzzy controller; (4) fuzzy controlled quantity output u(t) is obtained according to one-dimensional fuzzy rules; (5) u(t) is acted by the output scale factor Ku of the one-dimensional fuzzy controller and amplified to the actual controlled quantity on PI parameters; (6) u(t) is converted into a precise output value u0(t) and is overlapped on the PI parameters. The model reference self-adaptive system parameter self-tuning method based on the one-dimensional fuzzy control can be widely applied to the field of control over motors without position sensors.

The embodiment of the invention provides an augmented reality registration method and device, equipment and a storage medium, and the method comprises the steps: obtaining real space model data of a real space model, building a real space coordinate system corresponding to the real space model, and enabling the real space model to comprise a real biological model and a real three-dimensional reference model; according to the real space model data, creating a virtual space model corresponding to the real space model, creating a virtual space coordinate system corresponding to the virtual spacemodel wherein the virtual space model comprises a virtual biological model and a virtual three-dimensional reference model; and registering the virtual biological model and the real biological model based on virtual-real registration between the virtual space coordinate system and the real space coordinate system. According to the embodiment of the invention, high-precision virtual-real automaticregistration in augmented reality is realized.