34 results about "Mobility model" patented technology

The invention relates to an Internet of Vehicles multi-domain intelligent optimization method and system under a computing network convergence architecture, and the method comprises the steps: receiving a task request sent by an edge layer road side unit based on a pre-configured cloud edge end cooperation architecture, optimizing the user service time delay and network load balance through a differential evolution algorithm, and obtaining a network load balance optimization result. Generating an original intelligent model distribution path and distributing the pre-trained original intelligent model to an edge layer road side unit; receiving an original intelligent model and vehicle mobility data, optimizing vehicle mobility, model quality and alliance establishment cost through an alliance game algorithm, and constructing a vehicle fine-tuning alliance structure suitable for federal learning; on the basis of a vehicle fine tuning alliance structure, local model updating data and trust evaluation data of vehicles in an alliance are obtained, dynamic weighted federated learning is executed through multi-dimensional trust evaluation, security is enhanced through differential privacy and a block chain tracking mechanism, and a high-precision global intelligent model is generated and deployed to the vehicles. Reliable support is provided for automatic driving, V2X communication and the like.

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.

A method for characterizing a carrier (10) which is equipped with a radio-transmitter (11) provides score values for the carrier to match each of several mobility models. The method may allow identifying a type of the carrier as that associated with a best-fit mobility model. The method may proceed by repeatedly executing a loop, each new execution of the loop delivering updated matching score values. The method is useful for monitoring a geographical zone, in particular for statistically analysing a traffic currently occurring in the geographical zone or characterizing a type of a transmitter-equipped carrier that is currently intruding into said geographical zone.

A method provides information about a radio-communication channel between a transmitter (11) and a receiver (12) for a target future time instant, while at least one of the transmitter and receiver is moving. Information is inferred from at least one current or past mobility state of the pair formed by said transmitter and receiver, combined with a mobility model function relating to said pair. The method is useful for adjusting parameters of a communication session ongoing between said transmitter and receiver. It may also be useful for a radio-detection system suitable for characterizing radio interference due to a transmitter-equipped carrier (10).

The invention discloses a mobility extraction method, system and equipment of a low-temperature CMOS (Complementary Metal Oxide Semiconductor) and a storage medium, which are corresponding schemes: on the premise that a mobility model does not need to be preset, effective carrier mobility can be accurately extracted from standard I-V data, and errors caused by model dependence are avoided; moreover, different contributions of a strong field and a weak field to the mobility can be effectively distinguished and quantified, so that decoupling of a mobility physical mechanism is realized; meanwhile, drain-source parasitic resistance is extracted synchronously, interference of the drain-source parasitic resistance on mobility errors is avoided, and modeling consistency and physical integrity are enhanced; in addition, the method has good robustness and universality, is suitable for devices with different process nodes and different sizes, and can be conveniently integrated into an automatic modeling and evaluation process; and finally, the extracted mobility has clear physical parameter interpretability, can be directly used for tasks such as low-temperature circuit simulation, process analysis and device reliability evaluation, and has important engineering practical value.

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.

In some implementations, a first network device may receive, log data identifying user equipment (UE) mobility information for a set of UEs. The first network device may train a model of UE mobility based on the log data. The first network device may receive based on training the model of UE mobility, UE mobility information for a particular UE. The first network device may analyze, using the model of UE mobility, the UE mobility information for the particular UE to predict a location of the particular UE. The first network device may generate, based on predicting the location of the particular UE, a set of recommended cells for paging the particular UE. The first network device may transmit, to the second network device, information identifying the set of recommended cells.

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.

The invention relates to the technical field of vehicle-mounted edge computing, in particular to a load balancing task unloading method for a vehicle-mounted edge computing network, and aims to solve the technical problem that limited computing resources and energy limitation of a user terminal in a vehicle-mounted edge computing scene cannot meet the requirements of computation-intensive and delay-sensitive tasks. A reasonable task migration decision is realized by constructing a multi-user and multi-task mobile model, a communication model and a task unloading model of a single-edge server and a collaborative computing unloading algorithm based on deep reinforcement learning, and load balancing is realized among a plurality of edge servers, so that the task scheduling efficiency and the resource utilization rate are improved. According to the method, the response speed and the resource utilization rate of the system are improved, challenges in a dynamic environment can be effectively handled, and a new solution is provided for technical progress in the field of vehicle-mounted edge computing.

A method provides information about a radio-communication channel between a transmitter (11) and a receiver (12) for a target future time instant, while at least one of the transmitter and receiver is moving. Information is inferred from at least one current or past mobility state of the pair formed by said transmitter and receiver, combined with a mobility model function relating to said pair. The method is useful for adjusting parameters of a communication session ongoing between said transmitter and receiver. It may also be useful for a radio-detection system suitable for characterizing radio interference due to a transmitter-equipped carrier (10).

The present invention relates to a laparoscopy system (1) comprising a laparoscope (2) suitable for acquiring a laparoscopy image (12), a first laparoscopic instrument (3), a control unit (4), and a display unit (5). The control unit (4) is configured to provide a mobility model (22) of the first instrument (3), which determines the maximum mobility of the first instrument (3) in the body part (11) relative to its first pose (P1.3). Furthermore, the control unit is configured to receive control commands for a 3D model of the first instrument (3M) and to determine a virtual spatial movement of the 3D model of the first instrument (3M) using the received control commands and the mobility model (22) of the first instrument, wherein the virtual spatial movement of the 3D model (3M) of the first instrument is limited by the maximum mobility of the first instrument determined in the mobility model (22).The control unit is configured to generate a virtual 3D scene depicting the virtual spatial movement of the 3D model of the first instrument (3M), and based on this, to generate a virtual simulation representation showing a virtual camera view of the 3D scene from the perspective of the laparoscope (2). Furthermore, the control unit (4) is configured to generate a superimposed display (23) in which the laparoscopy image (12) is superimposed on the virtual simulation display. The display unit (5) is configured to display the superimposed display (23).

Embodiments of the present disclosure provide a solution for model monitoring for artificial intelligence / machine learning (AI / ML) based mobility. In a solution, a communication device obtains respective accuracy indications of a set of prediction results for a measurement event, wherein the set of prediction results are generated using an AI / ML model, a predication result is corresponding to a measurement result triggering reporting of the measurement event, and an accuracy indication of the prediction result is associated with the corresponding measurement result. The communication device determines a performance monitoring result of the AI / ML model based on the respective accuracy indications.

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.

The embodiment of the invention discloses a formation mobility correction method and device for an anisotropic formation inclined shaft. The method comprises the following steps: acquiring a hole drift angle, a tool face angle, a probe setting azimuth angle and pressure response data of a target inclined shaft at a target depth; calculating a target shape factor according to the hole drift angle, the tool face angle, the probe setting azimuth angle and the shape factor function; calculating the permeability ratio of the stratum at the target depth in the horizontal direction to the vertical direction; a correction function generated in advance based on the inclined shaft apparent fluidity model, the permeability model and the vertical shaft standard fluidity model is obtained; and calculating the target correction fluidity of the target depth according to the correction function, the target shape factor, the permeability ratio, the hole drift angle, the tool face angle, the probe setting azimuth angle and the pressure response data. According to the scheme, the fluidity of the anisotropic stratum inclined shaft can be corrected to a vertical shaft standard, and the same evaluation standard is provided for evaluating the stratum fluidity; and the scheme is high in correction precision and high in correction speed.

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 an unmanned aerial vehicle edge network control method, an unmanned aerial vehicle and a computer program product, and relates to the technical field of wireless communication, and the method comprises the steps: loading an unmanned aerial vehicle edge network scene model; loading a user terminal mobile model and a task generation model; loading a flight model and a coverage model of the unmanned aerial vehicle; loading a communication model of the unmanned aerial vehicle and the user equipment; loading a predefined energy consumption model and a target function; a seven-tuple of the POMDP model is loaded; obtaining observation information of an observation space under the current time slot; determining target decision action information based on observation information of an observation space under the current time slot, a seven-tuple, a Dyna environment model and an RSAC algorithm by taking minimization of calculation energy consumption of user equipment and maximization of an uplink rate of the user equipment as targets; and controlling the unmanned aerial vehicle edge network under the current time slot based on the target decision action information. The method is used for providing an unmanned aerial vehicle edge network control scheme.

The application provides a kind of unmanned aerial vehicle task unloading method and device based on deep reinforcement learning, it is related to vehicle networking technical field.The method comprises: according to the computing capacity of vehicle, task is handled by vehicle or edge server;If handled by edge server, the vehicle position is obtained by constructing the Gaussian-Markov mobility model of vehicle, according to the computing resource of unmanned aerial vehicle and the distance between vehicle and intelligent roadside facility, it is judged by unmanned aerial vehicle or by unmanned aerial vehicle and intelligent roadside facility;If by unmanned aerial vehicle, the path planning result of unmanned aerial vehicle is output according to DGBCO model;If by intelligent roadside facility, task allocation is optimized according to computing reuse technology;Unmanned aerial vehicle energy constraint problem based on Lyapunov optimization is constructed;MADDPG algorithm is used to obtain task unloading strategy.The application optimizes UAV trajectory, designs a kind of joint optimization method in combination with the dynamic change of the mobility and computing resource of vehicle, improves the comprehensive performance of system.

The application discloses a GaN HEMT physical-based high and low temperature noise model parameter extraction method, a GaN HEMT high and low temperature model based on a region division model, and establishes a physical-based noise model considering high and low temperature effects. The model is combined by high and low temperature pinch-off voltage model parameters, mobility model parameters, high and low temperature current model parameters, and high and low temperature model parameters of other key parameters, and a special high and low temperature noise characteristic calculation method is adopted. Compared with the existing noise modeling and parameter extraction technology, the noise model does not have fitting parameters, greatly simplifies the noise parameter extraction process, and can be applied to different environmental temperatures, and has high precision, so that more accurate and flexible guidance can be provided for low-noise amplifier circuit design in a complex working environment and device optimization.

The present invention belongs to the field of mobile wireless sensor networks and discloses a trust assessment method for mobile wireless sensor networks based on a generative adversarial network. A clustering model and a mobility model of sensor nodes in a mobile scenario are established; node communication, energy, data, and location changes are collected as trust evidence; a cluster head collects trust evidence of a target node and transmits it to a base station, preprocesses the trust evidence, separates labeled data from unlabeled data, trains using a semi-supervised learning method based on a generative adversarial network, and performs trust assessment on the node based on a softmax method; the assessment results are returned to the cluster head, which then makes a judgment on the target node based on the assessment results. The present invention introduces a generative adversarial network and a semi-supervised learning method, utilizes implicit information in a large amount of unlabeled data, reduces the detection model's dependence on labeled data, and accurately identifies malicious nodes using a small amount of labeled data, thereby ensuring the security of the mobile wireless sensor network.

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 invention discloses a task scheduling method for limiting task completion time delay and optimizing energy consumption in a complex environment. The method comprises the steps of obtaining task data, performing task unloading solution through a pre-trained task unloading model based on the task data, obtaining a task unloading path, and realizing task scheduling through the task unloading path. The establishment of the task unloading model comprises the steps of establishing a communication model, a calculation model, a mobility model and a task priority model which are matched with one another based on task scheduling for limiting task completion time delay and energy consumption optimization in a complex environment, and performing task unloading optimization solution with the task completion time delay and energy consumption optimization as the target. The invention discloses a task scheduling method for limiting task completion time delay and optimizing energy consumption in a complex environment, and a storage medium. On the basis of a heterogeneous linear multi-agent system, a dynamic elastic event triggering mechanism is introduced to save communication resources, and the efficiency of solving a distributed optimization problem is improved on the premise of ensuring the system security.

The present invention relates to a method for optimizing drone trajectories for information security, and belongs to the field of communication technology. The method comprises: establishing a system framework including multiple users, drones, no-fly zones, eavesdroppers, and targets; then, establishing a drone mobility model, a multi-user channel model, and defining performance indicators. Then, based on the eavesdropper position and channel characteristics, an interference model is established to reduce the eavesdropping effect while ensuring a safe distance between drones. On this basis, an optimization problem is constructed with the goal of maximizing communication rate and coverage and minimizing eavesdropping effects. The problem is decomposed using the block coordinate descent method, and it is solved iteratively in combination with the continuous convex approximation technology. By alternately optimizing the drone trajectory, updating variables and constraints, it is ensured that the algorithm converges to the global optimum. The method of the present invention improves the efficiency and security of task execution, and has important theoretical and application value.

This application provides a UAV edge network control method, a UAV, and a computer program product, relating to the field of wireless communication technology. The method includes: loading a UAV edge network scenario model; loading a user terminal mobility model and a task generation model; loading a UAV flight model and a coverage model; loading a communication model between the UAV and the user equipment; loading a predefined energy consumption model and objective function; loading the seven-tuple of the POMDP model; acquiring observation information of the observation space in the current time slot; determining target decision action information based on the observation information of the observation space in the current time slot, the seven-tuple, the Dyna environment model, and the RSAC algorithm, with the goal of minimizing the computational energy consumption of the user equipment and maximizing the uplink rate of the user equipment; and controlling the UAV edge network in the current time slot based on the target decision action information. This method is used to provide a UAV edge network control scheme.

A method, apparatus, and computer program product for augmenting the training of an autonomous travel model based on map-based annotations. The method includes augmenting an original training set with map-based annotated modification scenarios, at least one of which affects the route and at least one of which does not affect the route. The method includes generating modification scenarios that introduce synthetic route-changing objects into a feature map representing the original travel or travel scenario, which induce a travel path different from the original route, and other modification scenarios that introduce non-route-changing objects into the feature map, within which the original route is applicable. An autonomous travel model for predicting a travel path within a road segment based on the feature map representation is trained using the modification scenarios.

The invention provides a human activity intensity prediction method based on generalized spatial heterogeneity learning. The human activity intensity prediction method comprises node-to-node propagation learning, node-to-partition propagation learning, a graph learning module guided by mobility, and combination of a mobility model for spatial interaction modeling and graph information propagation. Introducing a radiation model to calculate a movement probability matrix between nodes, and capturing a random process of a local mobility decision; the migration volume of inflow and outflow is introduced to optimize fitting of motion possibilities; the problem of local heterogeneity is solved from the perspective of mobility cost; using a spectrum attention module to dynamically group nodes based on node attributes, and performing adaptive adjustment according to a time evolution mode; establishing dynamic interaction between nodes and partitions by using linearized transformer attention, and taking spatial partition features as additional channel parameters; according to the method, the prediction precision is improved, the real human movement rule can be more effectively captured and simulated, and the prediction result is more practical and reliable.

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.