12 results about "Mobility model" patented technology

The invention provides a Gummel curve simulation method and system and electronic equipment, and relates to the technical field of semiconductor device emulation.The method comprises the steps that according to the actual structure of a variable-component SiGe HBT device, division is conducted in combination with a preset grid division strategy, and a discretized grid is obtained; according to the material attribute of the device, carrying out physical attribute binding on each grid point in the discretized grid to obtain a material definition result of the discretized grid; respectively carrying out conventional conductive type doping and component doping on the device to obtain spatial variation doping distribution of the device; combining a preset composite model and a preset mobility model to obtain a physical model set of the device; and a Poisson equation and a carrier continuity equation are solved in combination with numerical values, and a Gummel curve of the variable-component SiGe HBT device is obtained. According to the method, the actual current-voltage characteristics of the device can be accurately reflected, and the core requirement of high-precision simulation is met.

The application belongs to the technical field of mobile communication, and particularly relates to a task migration method based on multi-point cooperation in mobile edge computing; the method comprises the following steps: constructing a network system model under a MEC scenario; constructing a cooperative communication model, a task computing model, a user mobility model and a load balancing model based on the network system model under the MEC scenario; constructing an offloading decision and a cooperative set association decision joint optimization problem according to the cooperative communication model, the task computing model, the user mobility model and the load balancing model; on a slow time scale, facing future load changes, performing node cooperation to form a cooperative set; solving the offloading decision and the cooperative set association decision joint optimization problem on a fast time scale according to the cooperative set to obtain an optimal offloading decision and an optimal cooperative set association decision; and the system performs task migration according to the optimal offloading decision and the optimal cooperative set association decision; the application can effectively reduce a task migration rate and a task execution time delay.

The embodiment of the application discloses a formation mobility correction method and device for anisotropic formation deviated well. The method comprises the following steps: acquiring the deviation angle, tool face angle, probe seat sealing azimuth angle and pressure response data of the target deviated well at the target depth; calculating the target shape factor according to the deviation angle, tool face angle, probe seat sealing azimuth angle and shape factor function; calculating the permeability ratio of the horizontal direction and the vertical direction of the formation at the target depth; acquiring the correction function generated in advance based on the deviated well apparent mobility model, permeability model and straight well standard mobility model; and calculating the target correction mobility of the target depth according to the correction function, target shape factor, permeability ratio, deviation angle, tool face angle, probe seat sealing azimuth angle and pressure response data. The scheme can correct the mobility of the anisotropic formation deviated well to the straight well standard, and provides the same evaluation standard for evaluating the formation mobility. Moreover, the correction precision and speed of the scheme are high.

A real-time resource allocation method for IEEE 802.11be WiFi based on deep deterministic policy gradient includes the following steps: 1) establishing an IEEE 802.11be WiFi network model; 2) determining the mobility model, path loss model, and interference model adopted by the network; 3) deriving the network throughput expression; 4) proposing an optimization problem with maximizing the minimum throughput as the objective function. This optimization problem aims to optimize the allocation of power, channels, and resource units in real time, thereby improving the network's minimum throughput; 5) designing a real-time resource allocation algorithm based on deep deterministic policy gradient to solve the optimization problem, realizing real-time resource allocation for IEEE 802.11be WiFi. This invention can effectively improve the network's minimum throughput.

Disclosed is a technique for designing a codebook for wireless communication between vehicles by executing a codebook design program on a codebook design apparatus. Based on a road structure, traffic parameter information, a mobility model, and a communication range R required for V2V communication, a vehicle distribution function representing the distribution of vehicles on the road is obtained. Based on the vehicle distribution function, beamwidths of the pattern to be generated by each codeword of the codebook are set. Then, codewords optimized for the hardware of the communication system of the vehicles on a road are designed based on an ideal beam pattern that generate the set beam width.

This invention discloses a hierarchical mobility management method and system for satellite-ground collaboration in low-Earth orbit (LEO) satellite networks. It primarily addresses the problems of excessive mobility signaling overhead and over-reliance on ground stations leading to network robustness issues in existing technologies for user mobility management. The solution involves: selecting key nodes for deploying Mobility Management Functions (AMFs) through network deployment planning to eliminate the maintenance overhead of virtual clusters; decoupling location updates from high-speed satellite movement by constructing dynamic activity areas for users and implementing a lazy location update strategy; establishing a mapping relationship between user identifiers and multiple nodes to achieve distributed on-board storage of user data, avoiding strong dependence on ground stations; and establishing a hierarchical paging mechanism, calculating the PCI index based on the user mobility model, and dynamically selecting paging strategies to achieve an optimal balance between paging overhead and paging latency. This invention can significantly reduce the overall signaling interaction cost while ensuring service continuity and eventual data consistency, effectively improving the robustness and autonomous operation efficiency of large-scale LEO satellite networks in scenarios with limited satellite-ground links, and can be applied to satellite communication networks.

The invention provides user equipment and a method for performing mobility prediction by using artificial intelligence. In one novel aspect, UE mobility prediction is performed through machine learning techniques based on UE measurement data. In an embodiment, the UE obtains a set of mobility-related data, inputs the set of mobility-related data into a mobility AI model for UE mobility prediction, and obtains UE mobility prediction based on the mobility AI model. In an embodiment, UE mobility prediction is range based. In another embodiment, two independent AI models are applied to predict UE mobility in different cases, such as within and not within the service area. In an embodiment, the UE obtains mobility feedback from one or more UE applications and fine-tunes the mobility AI model based on the mobility feedback.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

This invention discloses a design method for a highly sensitive graphene temperature sensor, belonging to the field of micro-nano sensing technology. The method includes the following steps: S1, constructing a graphene geometric structure model and optimizing the structure; S2, introducing electron-phonon coupling to optimize the structure; S3, calculating the band structure based on the optimized structure; S4, calculating the temperature at a given temperature based on the band data. v F S5, based on v F S6. Calculate resistivity; S7. Set different thermodynamic temperatures and repeat steps S2 to S5; S8. Fit the resistivity-temperature change curve and calculate the temperature coefficient of resistance; S9. Construct a high-sensitivity graphene temperature sensor based on the temperature coefficient of resistance. The method of this invention does not rely on mobility models or empirical fitting parameters. It can self-consistently and quantitatively predict the temperature resistance characteristics of graphene based on the intrinsic response of the electronic structure, providing a theoretical basis for the design of high-sensitivity graphene temperature sensors.

The present application proposes a human activity intensity prediction method based on generalized spatial heterogeneity learning, including node-to-node propagation learning and node-to-partition propagation learning, using a mobility-guided graph learning module, combining a mobility model modeling spatial interaction with graph information propagation; a radiation model is introduced to calculate the movement probability matrix between nodes to capture the random process of local liquidity decisions; the migration amount of inflow and outflow is introduced to optimize the fitting of movement possibility; the local heterogeneity problem is solved from the perspective of mobility cost; a spectrum attention module is used to dynamically group nodes based on node attributes, and to adaptively adjust according to the time evolution mode; a linearized transformer attention is used to establish dynamic interaction between nodes and partitions, and the spatial partition features are used as additional channel parameters; the present application improves the prediction accuracy, can more effectively capture and simulate the real human movement rules, and makes the prediction results more realistic and reliable.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

This invention relates to a method for optimizing the characteristics of AlGaN / GaN high electron mobility transistor devices by determining the polarized Coulomb field scattering intensity based on the calculated average momentum relaxation rate, belonging to the field of transistor device research technology. Starting from actual test results, this invention utilizes commercial semiconductor device simulation software to solve complex and coupled mathematical and physical equations. Through Monte Carlo simulation, it continuously optimizes the strong-field mobility model in the software, iteratively deriving simulation results consistent with actual devices. Based on this, the average momentum relaxation rate of polarized Coulomb field scattering is calculated. Targeted analysis is used to verify the feasibility of the method, thereby deriving the scattering intensity law of polarized Coulomb field scattering under strong fields, and predicting and optimizing the device performance of AlGaN / GaN HEMTs.