165 results about "Energy constraint" patented technology

The invention discloses a method for measuring the propagation time of an ultrasonic echo. The propagation time can be obtained by adopting corresponding fundamental wave signals aiming at different flow rates to carry out cross-correlation operations and search the peak values of cross-correlation functions. As the fundamental wave signals at different flow rates are actual data extracted in a dynamic measurement process, the fundamental wave signals have higher precision and are capable of reflecting actual conditions. The fundamental wave signals at corresponding flow rates are adopted to carry out the cross-correlation operations, thereby, the propagation time of dynamic ultrasonic signals is calculated, the measuring errors are greatly reduced, and the uncertainty of a measuring result is reduced. Moreover, the cross-correlation operations are carried out by utilizing the similarity among the signals, the higher the similarity is, the higher the precision, and a non-correlation noise suppression effect is obvious. The invention also provides a novel fundamental wave extraction mode, which is characterized in that the fundamental wave signals are accurately obtained through a set energy constraint boundary. Compared with the mode that a threshold method is adopted to obtain the arrival time of the echo so as to extract the fundamental wave signals, the initial segments of the fundamental wave signals extracted by the novel fundamental wave extraction mode disclosed by the invention are more accurate.

The invention discloses a phased array radar task scheduling method which specifically comprises the following steps of: summarizing all radar events; acquiring all radar events which can be executed at the moment pointed by the current time pointer tp; calculating the integrated priority of the radar events; and acquiring the radar events meeting resource constraint conditions at tp to obtain the final even type set. With the method, the resident event wave beam waiting period is fully utilized through the pulse interlacing technology and the time pointer, the resource utilization rate of a radar system is effectively increased, and the task missing rate is reduced; and the pulse interlacing time and the energy constraint conditions are considered under different waveform parameters, and the demand that the transmitted waveform needs to be adaptively changed under complex changeable environments can be satisfied.

Adaptive power management within media delivery system. Power management is performed for one or more devices within a media or signal delivery system. Depending upon one or more local and / or remote considerations associated with one or more of the devices within the system, various processing operations may undergo appropriate provisioning among the respective devices. Considering devices that are battery-powered, certain processing operations that may be highly power or energy consumptive may be offloaded to other devices having sufficient power or energy to effectuate such operations or that are not so limited or constrained by power energy (e.g., being wall powered or non-battery-powered). Operation of one or more of the devices in compliance with a power or energy constraint may be directed by a power management application resident on one or more of the devices within the system.

A method and device for generating an optimum vertical path intended to be followed by an aircraft. The device comprises at least one database relating to fixed and moving obstacles, a data entering unit, a data processing unit implementing iterative processing to generate an optimum vertical path between an initial state and a final state as a function of flight strategies, that optimum vertical path being generated in such a manner as to be free of any collision with surrounding obstacles and to conform to energy constraints, and a data transmission link for transmitting that optimum vertical path to at least one user system.

The invention provides a single-edge calculation server and multi-user collaborative calculation unloading and resource allocation method. The method comprises the following steps: firstly, performingmathematical modeling on user equipment in a network, calculation resources and calculation tasks of an edge server and the like; secondly, modeling the task cost composition and the execution cost of the task on the user equipment and the edge server, and modeling the problem as an optimization problem; then, dividing the problem into two sub-problems, solving a resource allocation method for minimizing the local execution cost of the task by using a derivative and function monotonicity, and solving a resource allocation method for minimizing the execution cost of the task on an edge serverby using a genetic algorithm; and finally, for the solved edge server resource allocation method, two methods of time energy constraint and local execution cost comparison are used for screening, anditerative computation is performed to obtain an unloading strategy. According to the method, the execution cost of the calculation task can be minimized, and the service quality of the user is improved.

Routing techniques are provided that meet performance objectives associated with an ad-hoc network environment and the like. The techniques of invention serve to substantially maximize the lifetime of the network. In one aspect of the invention, a packet routing technique for use in a node of a distributed network comprises the following steps / operations. Queues for storing packets are maintained, wherein at least one queue is associated with a link existing between the node and a neighboring node, and a queue has a height associated therewith. A route is then determined for one or more packets stored in the queues based on heights of queues at neighboring nodes, such that energy constraints associated with the node and the neighboring nodes are substantially maximized. As mentioned, the distributed network is preferably a mobile ad-hoc network wherein the node and the at least one neighboring node communicate over a wireless link.

The invention relates to an energy constraint heterogeneous reservoir thickness identification system. The system comprises the steps of acquiring accurate reservoir data parameters by single well logging; acquiring an optimal wavelet; constructing a reservoir basic wavelet function; constructing a basic function of energy constraint reservoir thickness spectrum, wherein psi is a wavelet function, t is time, b is the time difference between a top and a bottom interface, a is a scale parameter of different time and the scale parameter varies with frequency, and c and d are cosine square fringing attenuation functions; building the energy constraint reservoir thickness spectrum of a seismic trace near the well; carrying out three dimensional data space thickness prediction; with the aid of the machine three dimensional display system software of a computer, carrying out stereoscopic display on the reservoir thickness data obtained by the prediction.

The invention discloses a method for scheduling an energy storage system in a micro-grid. The method comprises the following steps of: discretizing the state of the energy storage system in the micro-grid; forming a target function by using the operating cost of the micro-grid in a cycle; setting output and climbing constraint conditions of a distributed power supply, power and energy constraint conditions of the energy storage system, a power balancing constraint condition of the micro-grid and the like; and designing a dynamic programming algorithm, and on the premise that the constraint conditions are met, acquiring an optimal scheduling scheme of the energy storage system. For the characteristics of the micro-grid, the energy storage system is optimally scheduled, the operating cost of the micro-grid can be reduced, the permeability of renewable energy sources is improved, and benefits of the energy storage system are maximized.

A data processing system attributes energy consumption to individual program segments or threads includes a processor that executes a first thread during a first portion of a measurement interval and a second thread during a second portion of the interval. An energy monitor measures the total energy during the interval. Energy attribution code attributes a first amount of the total energy to the first thread and a second amount to the second thread based in part on the execution times of the threads. The code may define a range of possible energy values by determining maximum and minimum energy constraints for the threads. The invention may also be extended to a multiprocessor environment and to a simultaneous multithreading (SMT) processor. In addition, the process may be expanded to determine energy consumed by various peripheral units such as hard disk controllers and the like.

The invention provides a planning method for short-term and effective load work with the capability of increasing energy utilization efficiency of satellites. The planning method comprises following steps: model creation of an energy constraint condition, model creation of the energy constraint condition and an input condition affecting a satellite energy condition and creation of a charge-discharge condition model for a storage battery group. The invention discloses the planning method for short-term and effective load work and an application example. A mathematical model is capable of taking actual running parameters of each orbit ring in satellite running as an input condition for calculation. Compared with a conventional method which only takes a short-term and effective load work mode and work time constraints into consideration under the harshest condition, working modes of loads can be combined at will in one orbit ring. By setting work time and working time of loads at will, the satellites can be more practical.

A Decision Feedback Equalizer (DFE) system includes a DFE and a DFE coefficients processor. The DFE receives an uncompensated signal and operates upon the uncompensated input using DFE coefficients to produce an equalized output. The DFE coefficients processor formulates a channel estimate as a convolution matrix H. The DFE coefficients processor determines a Feed Back Equalizer (FBE) energy constraint based upon the channel estimate. The DFE coefficients processor relates the convolution matrix H to the DFE coefficients in a matrix format equation, the matrix format equation based upon the structure of the DFE, the convolution matrix, an expected output of the DFE, and the FBE energy constraint. The DFE coefficients processor formulates a recursive least squares solution to the matrix format equation and computes the recursive least squares solution to the matrix format equation to yield the DFE coefficients.

A method and apparatus for distributed routing at the network layer of a network is disclosed that integrates contention resolution properties from the MAC layer. In one embodiment, an energy constraint is used in routing at the network layer of a network to determine a first parameter representing the optimal maximum lifetime of a sensor network. If a network link for a transmission is idle, the node may then contend at the MAC layer of the network for a transmission slot across that link. During this contention period, each node is assigned a penalty parameter that is used to represent the probability of a transmission colliding with another transmission across a link in a contention region. As a result of this contention period, network traffic is transmitted from sensor nodes.

The invention relates to a grid scheduling method based on energy optimization, which includes: 1. energy resources are the research focus of scheduling and factors such as initial energy value, network bandwidth, etc. are taken into consideration; 2. the energy optimization is achieved to ensure minimum energy consumption in the resource scheduling and the energy consumption in the grid resource scheduling is divided into calculation consumption and network communication consumption; 3. the optimization of time span is taken into consideration so as to ensure the load balance of the resource in the process of energy optimization; 4. a grid scheduling method based on energy optimization with a comprehensive consideration of energy constraint and time constraint is proposed. The method has the advantages that: 1. the limitation of singleness of resource type in the traditional resource scheduling calculation is changed; 2. the energy optimization is taken as the research focus of the scheduling model; 3. the problem of load imbalance caused in the process of energy optimization is solved; 4. an energy cost function is defined on the basis of time span optimization and energy optimization and the grid resource scheduling method based on energy optimization is proposed on the basis of the cost function.

The invention provides a micro-grid frequency control method with the lowest power generation cost, and belongs to the technical field of electric system operation and control. The method comprises the steps that a respective power generation cost function and a power generation cost micro increasing rate function are established; active power output limit constraints of distributed power supplies and energy constraints of energy storage devices are set; and the synchronization speed regulator of a power generator and the controller of an inverter are regulated to perform primary frequency modulation, secondary frequency modulation is performed after a time interval, system frequency is acquired and active power set value total regulating amount of each time of secondary frequency modulation is calculated, the active power set value total regulating amount is allocated for all the distributed power supplies or the energy storage devices participating in frequency modulation, the active power set value regulating amount and the original set values are summated to act as a new active power set value, then one time of frequency modulation is performed again and stable system frequency can be maintained. Utilization rate of renewable energy is enabled to be the maximum by the method, and consumption capacity of micro-grid renewable energy can be enhanced. Besides, realization is convenient and control is easy.

The invention relates to a resource allocation and unloading decision-making method based on multi-agent architecture reinforcement learning, and belongs to the technical field of mobile communication. According to the method, excitation constraints, energy constraints and network resource constraints are considered, wireless resource allocation, computing resource allocation and unloading decisions are jointly optimized, and a random optimization model for maximizing the QoE of a total user of a system is established and converted into an MDP problem. Secondly, according to the method, an original MDP problem is subjected to factorization, and a Markov game model is established; then, the method provides a centralized training and distributed execution mechanism based on an actor-evaluator algorithm. In the centralized training process, multiple agents obtain global information through cooperation, resource allocation and task unloading decision strategy optimization are achieved, andafter the training process is finished, all the agents independently conduct resource allocation and task unloading according to the current system state and strategy. According to the invention, theQoE of the user can be effectively improved, and the time delay and the energy consumption are reduced.

The invention provides an unmanned aerial vehicle data distribution optimization method under energy constraint. The method comprises the following steps: modeling based on an optimization problem ofunmanned aerial vehicle data distribution under an energy constraint; decoupling and converting the original optimization problem I into a new optimization problem II; dividing a problem 2 into two sub-problems: a trajectory optimization problem 3 and a transmitting power optimization problem 4, and updating the flight state of the unmanned aerial vehicle through trajectory optimization by fixingthe transmitting power of the unmanned aerial vehicle; the emission power optimization updates the emission power distribution of the unmanned aerial vehicle by fixing the flight state of the unmanned aerial vehicle; and alternately solving a trajectory optimization problem 3 and a transmitting power optimization problem 4, and outputting a result. Besides, the method is improved, the objective function of the fourth transmitting power optimization problem is replaced to be converted into the fifth problem, then the third problem and the fifth problem are alternately solved, and a result is output. According to the invention, the problems that ground Internet of Things equipment cannot transmit information remotely and data distribution is carried out under the assistance of an unmanned aerial vehicle when the communication stability is poor can be well solved.

The invention discloses a method for stabilizing the fluctuation of the output power of a renewable energy power supply in a power system. The method comprises the following steps of: forming a battery type energy storage power station by battery modules, and dividing the operating states of the energy storage power station according to the charging / discharging condition of the battery modules; according to forecast, giving a load predicted value and a renewable energy power supply output power predicted value of each time interval in a power stabilizing period, and adding the difference value between the load predicted value and the renewable energy power supply output powder to network loss to obtain the power difference value of the power system; setting the energy constraint condition of the energy storage power station and the gross capability constraint condition and climbing constraint condition of a normal unit; and designing a dynamic programming algorithm; with the goal that the sum of the absolute value of the gross output power change value of all time intervals of a thermal power unit in the system in one stabilizing period is minimum, obtaining the optimal scheduling scheme of the energy storage power station on the premise that the constraint condition is met. The method is used for stabilizing the fluctuation of the output power of the renewable energy power supply, so that the renewable energy power supply can be used to the greatest extent.

The invention provides an agile satellite task planning and scheduling modeling method based on genetic optimization, which comprises the following steps of: dynamically decomposing a to-be-observed complex observation target task area according to an observation demand of a user and a resource condition of a satellite, and generating a meta-task set with a plurality of time windows; constructinga constraint analysis model according to the task constraint, the resource constraint and the relationship between the task constraint and the resource constraint, wherein the constraint analysis model comprises a satellite energy constraint model, a storage constraint model, a minimum pitch angle constraint model and an attitude maneuver stability constraint model; establishing a single-satellitetask planning mathematical model according to the multi-time window element task set; and utilizing a genetic search algorithm to optimize and solve the single-satellite task planning mathematical model, judging whether the established constraint analysis model is met or not, and generating a final executable observation plan scheme. According to the method, the task observation plan scheme meeting the maximum objective function and different constraint analysis can be generated, satellite resources are effectively utilized, and the task planning efficiency is improved.

The invention provides an MIMO-STAP steady waveform design method based on incomplete clutter prior knowledge and belongs to the signal processing field. An iterative method is provided to solve the non-linear steady waveform optimization problem based on diagonal loading, each step of iteration is slackened to an SDP problem based on diagonal loading and therefore a convex optimization method can be used for solution. The method comprises steps: firstly, an MIMO-STAP model is established to obtain expression of space-time snapshot in an interesting distance ring; secondly, clutter gauss distribution is carried out, output SINR expression in an optimal MIMO-STAP processor condition is obtained in an interference irrelevant condition; thirdly, a waveform optimization problem raising the system detection performances is expressed under emission waveform energy constraint; fourthly, an iteration method based on diagonal loading is provided to solve the steady waveform optimization problem.

The invention relates to an underwater sound channel effective sound velocity estimating method based on intrinsic sound ray visual search. A sound ray emitted from a sound source is tracked according to different glancing angles, when a receiving point depth is achieved and the energy constraint condition is met, multi-route identification is carried out on the sound ray to obtain an equivalent sound velocity. After self-adaptive variable-step ergodic in an initial glancing angle effective space is finished, interpolation fitting is carried out on various multi-route reaching sound ray equivalent sound velocity. Finally, the equivalent sound velocities of sampling points in each space are compared to take a maximum value and obtain the effective sound velocity of the point, the intrinsicsound ray visual search of each sampling point in the space area is realized, the effective sound velocity of each sampling point is stored in a matrix, and the effective sound velocity of any two points can be quickly and accurately acquired through the interpolation fitting. The method ensures that the effective sound velocity can be accurately acquired when a complex multi-route reaches the structure by adopting the mechanisms of self-adaptive variable-step ergodic in the initial glancing angle effective space, energy restraining and multi-route identification. The intrinsic sound ray visual search mechanism is utilized to avoid the frequency calculation of a sound field and better fuse accuracy, the adaptability and velocity.

The invention discloses a region growth ultrasound image automation segmentation method based on energy constraint. The method comprises the following steps: (1) preprocessing is performed on an original ultrasound image, (2) automatic selection of a seed point is performed on the ultrasound image after the preprocessing, and (3) the seed point obtained in the step (2) is used to be taken as a start point of region growth, constrained growth is performed under an energy function, the growth stops when a constraint condition is reached, and then a final segmentation result is obtained. The obtained segmentation result is accurate, the segmentation process needs no artificial participation, fully automatic ultrasound image segmentation is really achieved, and subsequent processing such as focal region characteristic extraction is facilitated.

The invention relates to an embedded operating system task scheduling method for an energy collecting system. While task instantaneity is guaranteed, tasks are scheduled in a grouping manner by setting the maximum energy threshold value and the minimum energy threshold value of a battery. The embedded operating system task scheduling method for the energy collecting system has the advantage that energy consumption of the tasks and energy restriction of the battery are considered in a task scheduling process, and task execution interruption caused by insufficient energy of the battery can be avoided; and on the basis of the energy consumption of the tasks and the energy restriction of the battery, the grouping scheduling strategy is adopted, and the using frequency of the battery in a shallow charging and shallow discharging mode is reduced when a task load is high, so that the total energy transfer quantity of the battery and the energy service efficiency are improved.

Directly computing Feed Forward Equalizer (FFE) coefficients and Feed Back Equalizer (FBE) coefficients of a Decision Feedback Equalizer (DFE) from a channel estimate. The FBE coefficients have an energy constraint. A recursive least squares problem is formulated based upon the DFE configuration, the channel estimate, and the FBE energy constraint. The recursive least squares problem is solved to yield the FFE coefficients. The FFE coefficients are convolved with a convolution matrix that is based upon the channel estimate to yield the FBE coefficients. A solution to the recursive least squares problem is interpreted as a Kalman gain vector. A Kalman gain vector solution to the recursive least squares problem may be determined using a Fast Transversal Filter (FTF) algorithm.

The invention discloses a wind power integration unit combination method and device considering compressed air energy storage. The method comprises the steps that according to historical data, wind power output is predicted, a wind power output uncertainty set is constructed; the output constraint and energy storage energy constraint of each time period of a compressed air energy storage system are set; an optimized objective function by adopting the minimized cost as the target of an electric system is set; the power balance constraint, the backup constraint, the climbing constraint and the line power flow constraint needing to be met in each time period of the electric system are set; the output constraint and the minimum starting and stopping constraint of a thermal power unit are set;a robust unit combination model considering compressed air energy storage is constructed, and the robust unit combination model is obtained to obtain a robust unit combination strategy considering compressed air energy storage. According to the method, the compressed air energy storage unit combination strategy considering the wind power uncertainty can be obtained, so that the load modulation behaviors of the compressed air energy storage system are brought into full play, and the new energy absorptive proportion is effectively increased.

The invention discloses a relay selection method in an energy constraint cooperative communication system. The relay selection method comprises the following realization steps that (1) M source nodes utilize K energy constraint relay nodes to carry out cooperative communication with target nodes D; (2) one relay node with the least rest energy is found and is marked as R*; (3) the life cycle T<S1R> of a link from S1 to R* to D is constructed; (4) the source nodes are ranked according to the T<S1R> in sequences from small ones to large ones, a new set S is obtained, a node counter is set, in addition, the initial value m of the node counter is set to be zero; (5) m is increased by 1, and the life cycle T<SmRj> of the link is constructed; (6) the relay node corresponding to the maximum value of the T<SmRj> is marked as Rm; (7) steps (5) to (6) are repeated until m is equal to M, the relay nodes Rm are ranked according to subscripts in sequences from small ones to large ones, and a new set R is obtained; and (8) the resource nodes in the set S are selected to carry out cooperative communication with the relay nodes with the same subscripts in the set R. The relay selection method has the advantages that the energy efficiency is high, the realization complexity is low, and the relay selection method can be used for the cooperative communication system in energy constraint environment.

The invention discloses a method for improving MIMO-STAP detection performance through the design of emission waveform, and puts forward that the waveform optimization problem is simplified to be the SDP problem based on diagonal Loading, so that a convex optimization method can be utilized to find solution. The waveform optimization method can be realized by the following steps: (1) establishing an MIMO-STAP model so as to obtain space-time snapshot expression in an interest distance ring; (2) under the condition of clutter Gauss distribution, and interference uncorrelated, and under the condition of an optimal MIMO-STAP processor, outputting SINR expression; (3) under the condition of emission waveform energy constraints, expressing the problem of waveform optimization for improving the system detection performance; and (4) finding solution for the problem of waveform optimization based on a diagonal loading method.

The invention discloses a phased array beam dwell scheduling method based on a scheduling benefit and a genetic algorithm, belongs to the field of phased array radar resource management, and particularly relates to a method for implementing adaptive beam dwell scheduling by utilizing a task priority and a scheduling strategy. According to the invention, firstly, on the basis of a target threat model, target attribute information is expressed with a threat degree function; secondly, a two-dimensional priority list thought is utilized, a two-dimensional priority list is utilized for twice to integrate three parameters of a working mode priority, a deadline and a threat degree into a comprehensive priority in a nonlinear weighting mode, and uniqueness of the comprehensive priority is ensured;then the comprehensive priority is combined with a validity function of task scheduling to form a new scheduling benefit and accordingly, a scheduling optimization model is established, wherein a pulse interleaving technology and time and energy constraint conditions are considered; and finally, such nonlinear optimization problem is solved by the genetic algorithm. According to the phased arraybeam dwell scheduling method based on the new scheduling benefit, which is disclosed by the invention, a loss rate of a task can be effectively reduced, a hit value ratio and a correction value rate are improved, and meanwhile, an execution threat rate and a time utilization rate are also promoted.

The invention discloses a method for optimizing maximum MI waveforms of the cognition radar based on an IGA-NP algorithm. The method comprises steps of determining the cognition radar, wherein target signals exist in a detection range of the cognition radar, carrying out calculation to obtain target signal spectrum variance, clutter spectrum variance and a noise power spectrum and setting a to-be-optimized waveform power spectrum; determining an energy constraint condition of the to-be-optimized waveforms and carrying out problem modeling; setting iteration times and carrying out integar algebras of non-linearity programming optimization; setting the number of chromosomes, the crossover probability and the mutation probability; calculating an initial population Code; carrying out initialization: making h represent the hth iteration, wherein h belongs to {1, 2,..., G}, and G represents the set biggest evolution algebra and calculating the iterated k-opt searching population shown in the description and the optimal chromosome after the hth iteration; and finishing the iteration until the h=G, and using the corresponding optimal chromosome when the iteration is stopped as the global optimal chromosome, wherein the global optimal chromosome is the to-be-optimized waveform power spectrum.

The invention analyzes the security transmission rate of the SWIPT system in a full-dimensional antenna network by using theoretical tools such as a stochastic matrix and nonlinear optimization. An optimal 3D beam forming vector is acquired by maximizing the traversal transmission rate, and a system security transmission rate under the collected energy constraint is deduced. An optimal solution tomaximize the system security rate is obtained by combining a Lagrangian relaxation optimization algorithm with optimized transmitting power and power split ratio.

The invention relates to a method for segmenting a cardiac nuclear magnetic resonance image. The method for segmenting the cardiac nuclear magnetic resonance image comprises the following steps of: 1, performing Gaussian filtering preprocessing on the acquired cardiac nuclear magnetic resonance image; 2, computing an external force field of a generalized gradient vector flow based on expansion neighborhood and noise smoothing on the preprocessed image; 3, defining an initialized outline position of the left ventricular endocardium of the heart; 4, segmenting the left ventricular endocardium of the heart; 5, defining a final segmenting outline result of the left ventricular endocardium of the heart into an initialized outline position of the left ventricular epicardium of the heart; 6, setting the boundary strength of an area surrounded by the endocardium outline in an original boundary graph to be 0, recomputing the external force field of the generalized gradient vector flow based onthe expansion neighborhood and the noise smoothing; and 7, segmenting the left ventricular epicardium of the heart. Based on convolution computation, and by taking energy constraint of an elliptical shape, the method for segmenting the cardiac nuclear magnetic resonance image has the advantages of high computing speed, wide capturing range, strong anti-noise ability, excellent performance in weakboundary protection and deep dented region segmentation, and capability of accurately segmenting the left ventricular endocardium and the left ventricular epicardium of the heart.