26 results about "Adaptive randomization" patented technology

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention discloses an unmanned aerial vehicle path planning method based on an improved snake optimization algorithm, and the method comprises the steps: introducing a self-adaptive random disturbance factor based on a sine function to improve an exploration mechanism of an exploration stage and a development mechanism of a development stage in a first half iteration stage, dynamically enhancing the randomness of position updating in a search process, and positioning a global optimal solution; in the latter half iteration period, the development mechanism of the development stage is improved by introducing a scaling factor and leader-based adaptive Levy flight strategy, the male snake leader is endowed with flight ability and the global exploration ability is enhanced, and the development mechanism of the development stage is improved by introducing an adaptive position updating strategy combining elite leader and Brownian motion; the female snake leader is endowed with the Brownian motion ability, the convergence speed is effectively accelerated while the precision is ensured, and the local search ability is enhanced. According to the method, the path which is low in cost, high in safety and short in arrival time can be quickly found, and the task execution efficiency of the three-dimensional unmanned aerial vehicle is improved.

The invention discloses a method, a device and equipment for acquiring a global initial pose of a loading machine and a medium. The method comprises the following steps: acquiring original point cloud data of an operation scene in real time by using a vehicle-mounted laser radar to generate a corresponding frame point cloud; the method comprises the following steps: constructing a real-time feature vector set based on a fast point feature histogram feature of a frame point cloud, and performing nearest neighbor search on the real-time feature vector set and a k-d tree index of a pre-established global point cloud map to form an initial matching point pair set; in each round of iteration, screening a high-precision inner point set meeting geometric constraints by adopting a self-adaptive random sampling strategy, and calculating a candidate pose transformation matrix and a corresponding inner point proportion score according to the high-precision inner point set; and a new iteration round is repeatedly executed to execute the operation until the total number of iterations reaches a target value, and the global initial pose of the target loader is screened out from the candidate matrix according to multiple rounds of scores. According to the embodiment of the invention, high-precision global initial pose acquisition of the unmanned loader is realized in an environment lacking satellite coverage, and the positioning precision and reliability are improved.

The invention particularly relates to a parameter adaptive hybrid disturbance serial cancellation (SC) decoding method for polarization codes. Aiming at the problems of disturbance parameter fixation and rigidity, lack of channel adaptability and dependence on off-line parameter search in the existing hybrid disturbance serial cancellation (HPSC) decoding method, the method comprises the following steps: firstly, establishing a linear mapping relation between disturbance noise power and channel noise power to realize automatic adaptation of a channel environment; then, the check characteristic of a cyclic redundancy check code is used as a detection indicator of a local optimal trap, intelligent switching is carried out between adaptive random disturbance and adaptive biased disturbance, and a bias adjustment strategy dynamically changing along with the number of iterations and the reliability of a received symbol is introduced in the biased disturbance stage; therefore, parameter-adjustment-free high-performance decoding is realized on the premise of keeping low complexity of SC. Simulation results show that compared with a traditional HPSC decoding method with fixed parameters, the method provided by the invention has better block error rate performance, and has lower average decoding iterations in a high signal-to-noise ratio region.

The application provides an underwater vehicle optimization cooperative detection array position method, a target optimization function of an AUV cooperative detection array position is constructed by coupling AUV detection error and AUV position error, and the optimization object is minimization of global average detection error, so that passive detection AUVs construct an array position according to maximum cooperative detection accuracy; an improved HS-DPSO fusion algorithm is adopted to iteratively optimize the target optimization function; in the local optimization process, a dynamic inertia weight adjustment strategy combining normal distribution randomness and an adaptive expectation mechanism is adopted, so that the inertia weight regularly and dynamically fluctuates in each iteration, and the ability of the algorithm to jump out of a local optimum is enhanced; and a random number is used to dynamically adjust the adaptive random weight expectation value, so that the limitation of a manually preset fixed random weight expectation value is avoided, and the adaptability of the algorithm to different detection scenes is improved. The method makes each passive detection AUV quickly obtain an optimal cooperative detection position, and improves the cooperative detection efficiency and detection accuracy of the AUV formation.

This invention provides a method for optimizing cooperative detection array positions for underwater vehicles (AUVs). It constructs a target optimization function for AUV cooperative detection array positions by coupling AUV detection errors with their own positional errors, aiming to minimize the global average detection error. This ensures that passively detected AUVs construct array positions with maximum cooperative detection accuracy. An improved HS-DPSO fusion algorithm iteratively optimizes the target optimization function. During local optimization, a dynamic inertial weight adjustment strategy combining normal distribution randomness and an adaptive expectation mechanism is employed. This allows the inertial weights to fluctuate dynamically and regularly in each iteration, enhancing the algorithm's ability to escape local optima. Furthermore, the adaptive random weight expectation value is dynamically adjusted using random numbers, avoiding the limitations of manually pre-setting fixed random weight expectation values ​​and improving the algorithm's adaptability to different detection scenarios. This method enables each passively detected AUV to quickly obtain its optimal cooperative detection position, improving the efficiency and accuracy of AUV formation cooperative detection.

The invention relates to the technical field of intelligent medical monitoring, and discloses a sleep disorder intelligent monitoring system and method based on a multi-modal physiological signal, and the method comprises the steps: collecting electrocardio, respiration and body movement signals, calculating a finite time Lyapunov index by using a phase-space reconstruction technology, taking the finite time Lyapunov index as a feedback control variable, and calculating the sleep disorder intelligent monitoring system based on the multi-modal physiological signal; noise intensity in a Langevin equation is dynamically adjusted, adaptive stochastic resonance enhancement is performed on the preprocessed signal, weak characteristics are improved by using noise energy, and causal coupling intensity between cross-modal symbol transfer entropy and net information flow index quantification systems is further calculated through phase-space symbolization mapping. And in combination with Lempel-Ziv complexity, a multi-dimensional judgment logic is constructed, and sleep staging and obstacle event results are output. According to the method, the problems that nonlinear physiological signal features are difficult to extract and a system coupling mechanism is unknown are solved, and the robustness and accuracy of sleep monitoring are improved.

The invention provides a recurrent event clinical test sample size estimation method and device, equipment and a medium, and relates to the technical field of clinical test design and biostatistics. According to the method, test basic information is acquired, a statistical model integrating a subject falling mechanism is established based on the test basic information, then key bridge parameters and average exposure time are calculated, and a double-path closed-loop decision including a design stage and an execution stage is executed; calculating a target distribution probability based on the key bridge parameters and the average exposure time so as to calculate a total sample size; and dynamically updating parameters based on the current real-time data in the execution stage, and carrying out self-adaptive random allocation on new subjects. According to the method, the influence of falling of the subject on the exposure time can be explicitly modeled, parameter unified driving sample size estimation and dynamic randomization distribution are introduced, the benefit of the subject is optimized while the inspection effect is ensured, and integrated decision support from design to execution is provided.

The invention relates to the technical field of protection engineering, in particular to a facility system first-aid repair resource allocation method for a complex uncertain attack scene, which comprises the following steps of: S1, acquiring a facility system network structure and a facility number, and evaluating a facility initial function; s2, determining the level of an adverse event; s3, determining facility system first-aid repair resource preset planning constraint conditions, and constructing an improved facility system first-aid repair resource preset planning model; and S4, solving the first-aid repair resource preset planning model by adopting an adaptive random search algorithm (ARS-SVM) fused with a support vector machine to obtain an optimal solution of the first-aid repair resource configuration of the facility system. According to the method, the resource demand satisfaction degree expectation which maximally considers the conditional value-at-risk is taken as a target, the demand is regarded as a random variable, the probability distribution is obtained through historical data fitting, and the auxiliary variable is introduced to quantify the demand fluctuation, so that the urgent repair recovery accuracy can be improved, the cost effectiveness can be balanced, and the system adaptability can be enhanced.

The invention relates to the technical field of laser coherent synthesis, discloses a self-adaptive stochastic parallel gradient descent parameter optimization algorithm based on reinforcement learning, and solves the problems that a traditional parallel gradient descent algorithm (SPGD) is slow in convergence and the effect of an improved parallel gradient descent algorithm depends on manual appointing of hyper-parameters. The method comprises the following steps: inputting performance indexes measured after coherent combination of multiple beams of laser into a reinforcement learning parameter optimization algorithm to obtain SPGD parameters, then executing the SPGD algorithm according to the updated parameters to obtain the performance indexes of the current parameters, and repeating the process for multiple times until the reinforcement learning algorithm converges to obtain the performance indexes of the current parameters. The optimal SPGD parameters under different conditions can be stably output, so that the SPGD algorithm can quickly correct errors, and high-coherence synthesis output is realized. The adaptive stochastic parallel gradient descent parameter optimization algorithm is simple in structure, easy to implement and efficient, and has high robustness, real-time performance and capability of adapting to complex environment change.

The invention discloses an RGBT tracking method based on diversified semantic aggregation. According to the method, on the basis of an RGBT tracking framework of a single-branch Transform, local semantics and global semantics of 1-12 layers of features are aggregated through a designed diversified semantic aggregation method to be used for target positioning so as to improve tracking accuracy. Specifically, firstly, a shallow random feature is obtained from a plurality of shallow features by adopting an adaptive random selection method, and then the shallow random feature containing local semantics and a plurality of deep features containing global semantics are subjected to semantic fusion in a cascade mode. Wherein semantic fusion takes a semantic convergence template as a medium to learn semantic information from significance information of a previous layer semantic fusion feature, and the semantic convergence template and a current layer feature are fused based on a double-branch cross attention enhancement method to obtain a previous layer semantic fusion feature and a current layer semantic fusion feature. Diversified semantic aggregation results not only provide rich semantics for target positioning, but also can promote tracking accuracy.

The invention discloses an ancient fresco element detection algorithm based on context semantics and adaptive augmentation, and belongs to the cross technical field of computer vision and cultural heritage protection. Comprising the following steps: constructing a Chinese ancient mural element target detection data set; designing an adaptive random erasure image augmentation model, and augmenting the constructed data set; constructing a lightweight multi-scale feature extraction backbone network, and performing feature extraction on the augmented image; constructing a semantic feature enhancement model, and performing semantic enhancement on the extracted features; the obtained enhanced semantic feature map is input into the full convolutional network, a detection result is output, the detection result comprises element categories, bounding box coordinates and confidence coefficients, experiments show that the detection precision of the method on a self-built data set reaches 87.5%, the single-map detection time is only 0.022 seconds, the mural elements can be efficiently and accurately recognized, and the method is suitable for popularization and application. And technical support is provided for ancient mural protection, historical research and digital inheritance.

The invention provides a federated learning communication method based on adaptive-random client selection and dynamic regularization. The method comprises the following implementation steps: a central server initializes federated learning communication parameters and sends the parameters; the central server adaptively and randomly selects an active client; the active client performs iterative training on the local model; and the central server obtains a federal learning result. According to the method, the active clients are selected by adopting a self-adaptive and random combined method, so that communication resources are prevented from being frequently consumed on the clients with relatively small data contribution at the initial stage of communication; after training enters a stable stage, active clients are randomly selected based on a fixed activation probability, so that overfitting is prevented, convergence speed and generalization ability are balanced more efficiently, coefficients of dynamic regularization items in loss values of a global model are calculated through weights of the active clients, and the robustness of the overall model is improved. Different clients are subjected to dynamic constraints of different degrees when executing local optimization, so that the convergence precision is improved while the training stability and robustness are ensured.

The application provides an arc fault detection method based on a completely self-adaptive random configuration network, which builds a network structure by self-adaptive learning of arc fault current characteristics, judges the redundancy of a newly added node to realize adjustment of the network width, judges the error level of the newly added node to realize adjustment of the network depth, and associates the network structure with the current signal, so that data driving is realized, the calculation complexity of the network is reduced, and accurate and rapid detection of the arc fault is realized.

A method for Bluetooth Low Energy (BLE) advertising includes determining, by a controller of a BLE device, a next transmission time for advertising content using a bounded mean-reverting random-walk process. The determination is based on a next drift variable (Δtn+1), a base interval value (T0), and a random jitter component (J). The next drift variable is determined based on a current drift variable (Δtn), a first parameter (λ), a random variable (ξn) and a scaling factor (σ) set to determine a standard deviation. The current drift variable and the next drift variable are constrained within boundaries (-Δmax, Δmax). The method further includes transmitting the advertising content, by a transmitter of the BLE device, autonomously at the next transmission time without external coordination. The bounded mean-reverting random-walk process may provide collision avoidance in dense beacon environments by enabling autonomous desynchronization between multiple BLE devices operating in proximity.

The invention discloses a dual-mode adaptive random model adjustment method, system and device and a medium, and the method comprises the steps: obtaining real-time state sensing data at each epoch of a Kalman filtering calculation process through monitoring a prediction error sequence of Kalman filtering and calculating a normalized prediction error square statistic; judging whether the monitoring point enters a high-dynamic mode or not; in response to the judgment of entering the high-dynamic mode, executing a model adjustment operation through a continuous adaptive control algorithm to obtain an adaptive filtering model for the high-dynamic mode; when the adaptive filtering model is adopted, judging whether a dynamic event is ended or not; in response to judging that the dynamic event is ended, recovering the filtering model to a state before adjustment; a filtering model in a current state is applied to original observation data for positioning calculation, a new prediction error sequence generated by calculation is fed back, and a continuous high-precision positioning result is obtained. According to the invention, timeliness and reliability of geological disaster monitoring and early warning are substantially improved.

The invention discloses a virtual power plant frequency modulation method based on adaptive random image descent, and the method is based on the actual demands of virtual power plant frequency modulation, takes a random image descent method as a basic theoretical tool, builds the virtual power plant frequency modulation method based on random image descent, and proposes a dynamic acceleration moment estimation technology to improve the random image descent method. The improved random image descent method is used for quickly solving the virtual power plant frequency modulation model, the optimal control instruction and the expected deviation and control cost of the virtual power plant in the whole control period are output, the solution speed and precision are improved, and the problem that an accurate virtual power plant frequency modulation strategy cannot be formed in reasonable time through a traditional method is solved.

The invention discloses a wide-area time transfer method based on adaptive random noise constraint, and the method comprises the steps: obtaining the historical observation data of a receiver, and carrying out the calculation based on a precise single-point positioning model, and obtaining a receiver clock error sequence; performing variance analysis on the receiver clock error sequence, identifying dominant noise types under different smoothing times, and determining a time interval of segmented modeling; selecting a corresponding variance estimator or constructing a mixed variance model according to the dominant noise type, and calculating an adaptive random noise constraint value; and introducing the adaptive random noise constraint value into a process noise matrix of the precise point positioning model, and carrying out dynamic constraint estimation on a receiver clock error to realize time transfer. The method provided by the embodiment of the invention can overcome systematic deviation introduced by a traditional fixed constraint strategy, adapts to physical property differences of different atomic clocks, and remarkably improves receiver clock error estimation precision and frequency stability of wide-area time transfer.

The invention belongs to the technical field of triboelectric energy collectors, and relates to a triboelectric energy collector adaptive stochastic dynamics analysis method under high-dimensional excitation, which comprises the following steps: introducing a high-dimensional point selection strategy of uniformly distributed random variables, and determining a representative point assignment probability by using a uniform subdivision technology and a representative region weight index; constructing an adaptive iteration framework, establishing a convergence criterion based on a response statistical moment relative error, gradually increasing representative points from an initial point set, only performing physical response calculation on newly added points, and realizing sample scale automatic optimization through weight global update; a high-dimensional random excitation spectrum expression model and a probability conservation principle are combined, a nonlinear kinetic equation of the triboelectric energy collector is efficiently solved, and probability density functions and power reliability of system displacement, speed and voltage are directly obtained. According to the method, the probability density function and the reliability of the dynamic response of the high-dimensional nonlinear system can be accurately obtained, the calculation cost is remarkably reduced in a high-dimensional space, and the calculation precision is improved.

The embodiment of the invention provides a residual service life prediction method based on an adaptive random physical information neural network, and the method comprises the steps: obtaining equipment operation data; based on a feature extraction module, equipment degradation features are extracted from the equipment operation data, and the equipment degradation features comprise feature association information and time sequence dependence information; the feature extraction module is constructed on the basis of a time sequence context sensing Transform; based on the predictor, according to the equipment degradation characteristics, determining a residual service life prediction value; determining each order derivative of the equipment degradation characteristic; based on a self-adaptive random physical loss function, determining an input sample according to the residual service life prediction value and each order derivative of the equipment degradation characteristic; the adaptive random physical loss function is constructed based on a partial differential equation and a stochastic differential equation; and inputting the input sample into a prediction module to obtain the residual service life, and constructing the prediction module based on a meta-learning method. The technical scheme provided by the invention is used for solving the problems of few samples, black box effect and the like.

The invention discloses a smart phone real-time positioning method and system based on random forest optimization weight determination, and the method comprises the steps: firstly collecting the original observation data of a smart phone GNSS, carrying out the preprocessing, constructing a training set and a test set, carrying out the model training through a random forest model, and carrying out the real-time positioning of the smart phone. Fitting the training set by using the optimized random forest model to obtain a self-adaptive random model; secondly, extracting features which are strongest in correlation with observation noise from observation data, inputting the features into a self-adaptive random model, and predicting pseudo-range noise and carrier phase noise of each satellite; carrying out dynamic weight adjustment on the observation data based on pseudo-range noise and carrier phase noise output by the self-adaptive random model; and finally, positioning calculation is carried out based on the initial position, the double-difference observation equation and the dynamic weight adjustment result by using extended Kalman filtering, and a final positioning result of the moving station is obtained. The dynamic and high-precision prediction of the observation noise and the real-time optimization of the weight are realized.

The disclosed technology enables contention-free adaptive random access based on dynamic allocation of Random Access preambles. Upon retrieving usage data associated with multiple endpoint devices, a network node of a communication network predicts a demand for a connection associated with a service provided by the communication network by applying a model to the usage data. Based on the predicted demand for the connection associated with the service, the network node dynamically allocates a set of Random Access preambles for the connection.

The invention discloses an aluminum-based brake disc cross-scale modeling and microcosmic failure finite element analysis method, which comprises the following steps: firstly, acquiring enhanced particle parameters of a brake disc sample, generating REV through an adaptive random sequence extension algorithm, setting a zero-thickness cohesion unit and endowing the zero-thickness cohesion unit with material attributes; then, macroscopic transient thermal-machine-wear full-coupling analysis is carried out on the built brake disc full-size three-dimensional finite element model, and a brake disc global stress field, a temperature field and wear distribution are obtained; finally, inputting the macroscopic local load into the REV, applying a periodic boundary condition, and feeding back microscopic damage to correct the macroscopic model; and the micro thermal stress of the REV is solved, the stress distribution of an interface and a nearby area is analyzed, and the failure mode and mechanism of the aluminum matrix composite brake disc under the micro scale are determined. According to the method, through macro-micro cross-scale coupling analysis, the micro stress distribution rule of the reinforced particles of the aluminum-based composite material brake disc and the matrix interface is disclosed, and the failure mechanism of micro crack initiation and evolution is clarified.