32 results about "Multivariate optimization" patented technology

The present invention provides a method and system for optimizing building energy usage. The method comprising receiving a plurality of input values associated with a building or plurality of buildings. The method then constructs a thermal and an electrical load model based on the inputs and constructs an overall energy model, the overall energy model being based on the thermal and electrical load models. The method next generates a plurality of demand models and optimizes the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules. Finally, the method displays recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model, the determination based on client preferences.

Crop yield may be assessed and predicted using a piecewise linear regression method with break point and various weather and agricultural parameters, such as NDVI, surface parameters (soil moisture and surface temperature) and rainfall data. These parameters may help aid in estimating and predicting crop conditions. The overall crop production environment can include inherent sources of heterogeneity and their nonlinear behavior. A non-linear multivariate optimization method may be used to derive an empirical crop yield prediction equation. Quasi-Newton method may be used in optimization for minimizing inconsistencies and errors in yield prediction. Minimization of least square loss function through iterative convergence of pre-defined empirical equation can be based on piecewise linear regression method with break point. This non-linear method can achieve acceptable lower residual values with predicted values very close to the observed values. The present invention can be modified and tailored for different crops worldwide.

The method, according to one embodiment of the present invention, receives a plurality of input values associated with a building or plurality of buildings. The method then constructs a thermal and an electrical load model based on the inputs and constructs an overall energy model, the overall energy model being based on the thermal and electrical load models. The method next generates a plurality of demand models and optimizes the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules. Finally, the method displays recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model, the determination based on client preferences.

Total analysis systems and methods for simultaneously monitoring a suite of biological and / or chemical species in water and / or other process systems are disclosed. The system provides a sample-volume controlled sensor array comprising a fluid delivery device and a plurality of optical sensor elements for determining the presence and total concentrations of multiple analytes in the process system simultaneously. Image identification algorithms are provided for identifying the analytes based on image intensity, color pattern, positional arrangement, and the like. The methods incorporate multivariate optimization algorithms to analyze multiple sensor responses. This produces analytical results that are typically difficult to obtain without full system or variable compensation. The improved array response may then be utilized to measure, monitor, and control the concentration of analytes in the chemical or biological sample or water system.

The invention discloses a robust resource allocation algorithm of a cognitive heterogeneous wireless network and belongs to the technical field of communication cognitive heterogeneous wireless networks. The robust resource allocation algorithm is characterized by considering a robust resource allocation algorithm in a cognitive heterogeneous multi-network environment influenced by channel parameter perturbation, considering cross-layer interference constraint and bounded channel uncertainty and interference power uncertainty in an objective function, converting the original infinite dimensional multivariate optimization problem into the convex optimization problem of certainty by using a robust optimization theory, obtaining a robust power control algorithm by using a lagrangian duality method, and achieving sharing of the robust resource of the cognitive heterogeneous wireless network. Simulation results show that the algorithm can effectively protect the performance of users of a macro-cellular network and has stronger robustness.

The invention discloses a PSO (Particle Swarm Optimization)-based maximum wind energy capturing method of a VSVF (Variable-speed Variable-frequency) wind power system. Aiming at the VSVF wind power system which is formed by a variable-frequency transformer and multiple parallel-running permanent magnetic synchronous wind power generation units, cooperative optimization can be carried out on the rotating speed of the variable-frequency transformer and the pitch angles of all permanent magnetic synchronous wind power generation units through PSO, so that the capturing of maximum wind energy of the VSVF wind power system is realized. Compared with a univariate optimization method aiming at the rotating speed of the variable-frequency transformer, a PSO-based multivariate optimization method provided by the invention is capable of obtaining more wind energy.

The invention discloses an externally meshed gear pair which comprises two external gears. In polar coordinates, a single figure of the external gear I is sequentially composed of a cosine base compound curve which is composed of a line segment I, a cosine curve I, a line segment II and a cosine curve II. The line segment I, the cosine curve I, the line segment II and the cosine curve II meet first order continuity. A single conjugate figure of the external gear II corresponding to the single figure of the external gear I is obtained through a meshing principle and a conjugate gear pair is formed. A complete conjugate gear pair is obtained as the single figures of the external gear I and the external gear II respectively rotate around the center of the conjugate gear pair and evenly distributed according to the number of teeth of the conjugate gear pair. An asymmetric gear pump figure is obtained by adjusting symmetrical coefficients of the gear figure so that long sides in two gear sides of the gear are suitable for transmission, while short sides are suitable for seal. Separation of functions of transmission and seal are benefited and transmission and seal performance is enhanced. Fairness between the gear pairs can reach first order continuity and a mathematical model is set up for multivariate optimization of the gear pair.

The invention provides a power grid multivariate optimization scheduling method for improving the wind power acceptance capability, which comprises the steps of 1, predicting wind power through a windpower output model, and meanwhile, adjusting spinning reserve of the system; and 2, optimizing the output of each unit, realizing a nuclear power and hydrogen storage assisted joint optimization scheduling method under the condition of taking thermal power as a base load, realizing equivalent peak regulation of nuclear power, and realizing a wind-nuclear-storage-thermal power joint optimization scheduling process. The invention provides a method for improving the wind power acceptance capability through wind-thermal-nuclear storage optimization scheduling. Firstly, a wind power model of windpower uncertain output is established to realize the prediction condition of the wind power output; then a nuclear power peak shaving depth model is established, the nuclear power peak shaving depth is determined, a scheduling model based on joint optimization is established, thermal power, nuclear power and energy storage scheduling is analyzed and determined through an ant colony algorithm, energy transfer and peak load shifting are achieved, and clean energy output is maximized by increasing the utilization amount and utilization rate of clean energy.

Disclosed is a cold-rolling mill multivariate strip shape control method based on a constrained optimization algorithm. The method includes the steps that a multivariate optimization model used for solving an optimal regulating quantity of each strip shape regulating mechanism is designed; the multivariate optimization model is converted into a standard form of an optimization problem; an initial searching point for solving the multivariate optimization problem of the standard form is designed; a searching mechanism in the direction of each coordinate axis is designed; searching is performed in the direction of each coordinate axis, and a candidate value of an optimal point is calculated; a searching termination criterion is designed; and a cold-rolling mill multivariate strip shape control system model is established. By means of the cold-rolling mill multivariate strip shape control method based on the constrained optimization algorithm, the multivariate strip shape optimization model can be converted into a series of univariate optimization problems, and the method has the advantages of being high in calculation efficiency, saving the storage space of a controller and being capable of achieving concurrent computation and the like, and is quite suitable for development of a cold-rolling strip shape control system with the multiple strip shape regulating mechanisms.

A method and system for channel quantization for multiuser diversity are provided. A quantizer may utilize quantization levels determined based on an objective function J that corresponds to a performance metric for a finite-rate feedback multiuser downlink communication system. The quantization levels may be obtained based on the results of maximizing the objective function J by means of an optimization algorithm. The optimization algorithm may be a multivariate optimization algorithm that may be implemented utilizing an iterative approach. The quantizer may utilize the quantization levels to quantize channel state information (CSI) to achieve multiuser diversity in the finite-rate feedback multiuser system. The performance metric may be based on signal-to-noise ratio (SNR), bit error rate (BER), and / or system capacity. The quantization levels may be utilized in a multiuser system where distributed quantization is needed for optimal selection.

The invention discloses a conjugate gear pair based on a common rack, wherein each single tooth profile of the common rack is formed by a cosine-based composite curve composed of a straight line segment I, a cosine curve I, a straight line segment II and a cosine curve II in a rectangular coordinate system, and the straight line segment I, the cosine curve I, and the straight line segment II and the cosine curve II sequentially meet first-order continuous requirements. Each single conjugate tooth profile, corresponding to each single tooth profile of the common rack, of an inner gear or an outer gear is obtained through the meshing principle, and therefore the conjugate gear pair is formed. The whole conjugate gear pair is formed by the single tooth profiles, which are evenly distributed around the center of the inner gear or the outer gear according to the number of teeth of the inner gear or the outer gear, of the inner gear or the outer gear. According to the conjugate gear pair based on the common rack, through the adjustment of the symmetric factor of the tooth profiles, the non-symmetrical gear pair can be obtained, and therefore the long side in the two tooth sides is more suitable for transmission, the short side is more suitable for being used as a seal when the tooth profiles are used as a pump, the separation of a transmission function and a sealing function is benefited, the transmission performance and the sealing performance are enhanced, and a design method of the conjugate gear pair based on the common rack builds a mathematical model for the multivariate optimization of gear pairs.

The present disclosure relates to a method and an ad decision sever for determining personalized messages during a provisioning of digital content. The method comprises to provide a set of personalized messages candidates by executing a relevance algorithm, which matches message metadata and content metadata. The method continues with receiving calculated network costs for the set of personalized message candidates in order to determine a set of personalized messages, which are optimized with respect to the received network costs by applying a multivariate optimization algorithm.

The invention relates to the technical field of logistics freight, and relates to a railway freight intelligent assembly method, which comprises the steps that basic data of goods are input into a railway freight intelligent assembly system, the basic data comprise the shape, the size and the number of the goods, and the railway freight intelligent assembly system calculates and analyzes constraint conditions of the goods according to the basic data of the goods; during assembly, according to the vehicle body condition of the transport vehicle, the vehicle body is assembled; vehicle body stowage constraint conditions in the railway freight intelligent assembly system are called; and according to the constraint conditions of the goods and the vehicle body stowage constraint conditions, utilizing a multivariate optimization algorithm to calculate a stowage plan of the goods in the transport vehicle, and according to the stowage scheme, generating a stowage graph of the goods loaded on the transport vehicle. The method has the advantages that reasonable and efficient loading and stowage are calculated according to cargo attributes and vehicle body specifications, the stowage efficiency is improved, and the work of bag overturning after manual loading is reduced.

The invention discloses a closed-loop control method of cold-rolled sheet shape based on lag compensation. The closed-loop control method comprises the specific steps of manufacturing a structure of a sheet shape closed-loop control system based on a multivariate optimization model; designing the multivariate optimization model of the sheet shape closed-loop control system; manufacturing PID control models of all sheet shape regulating mechanisms; manufacturing PID+Smith predictor control models of all the sheet shape regulating mechanisms; determining process models of control circuits of all the sheet shape regulating mechanisms, and setting corresponding PID parameters; and establishing control modes of the sheet shape closed-loop control system. The closed-loop control method has the advantages that the influence of measurement lag to the stability of the sheet shape closed-loop control system during sheet shape control process can be removed through switching the sheet shape closed-loop control modes according to the rolling speed change, the structure is simple, the calculation efficiency is high, the control real time performance is relatively high, the stability of the sheet shape closed-loop control system is not influenced by measurement lag, and relatively high homeostatic control accuracy is achieved.

The invention discloses a multi-sensor distributed data fusion method based on the Chernoff fusion criterion. According to the method, particle filtering is performed on each sensor to obtain local estimation results firstly, the local estimation results are approximated as Gaussian mixture distribution through adoption of the maximum expectation algorithm, Gaussian mixture parameters are interacted among the multiple sensors, initial fusion results of the multiple sensors are obtained through utilization of a Chernoff fusion method in the case of a first-order approximation model, importance sampling is performed on the results, a multivariate optimization function with a constraint is built, the optimization function is solved through adoption of a particle swarm optimization algorithm, index values of the multiple sensors under the Chernoff fusion criterion are obtained through calculation, Chernoff fusion is finally performed by utilizing particle samples and the optimal index weight of the Chernoff fusion, and then an estimation state of a target is obtained through calculation. The method theoretically, approximately and optimally solves the problem that ideal distributed fusion results for the multiple sensors are difficult to obtain in the case that correlation between the local estimation results exists.

The invention provides a D2D-based downlink power allocation method with high energy efficiency in a distributed antenna system. An effective downlink power allocation scheme is researched mainly forthe D2D-based distributed antenna system, the energy efficiency of D2D communication is optimized, the D2D multiplexes the downlink spectrum resources of a CU in an underlay mode in the system, so that both of a CU receiving end and a D2D receiving end are interfered, therefore in order to ensure the normal cellular communication quality, the minimum rate requirement of the CU is considered when the scheme is designed; an energy efficiency optimization problem using the distributed antenna power and the D2D transmitting power as independent variables is constructed; the original multivariate optimization problem is converted into a univariate optimization problem by using a KKT condition to obtain a closed-form solution of the optimal power; then, a power allocation algorithm based on a traversing method is designed, and the effectiveness of the algorithm is proved; and the whole method is simple and feasible, and is easy to implement.

The present invention provides a D2D-based downlink energy-efficient power allocation method in a distributed antenna system, mainly for the D2D-based distributed antenna system, researching effective downlink power allocation schemes, optimizing the energy efficiency of D2D communication, In the system, D2D multiplexes the downlink spectrum resources of CU in underlay mode, which causes interference to both the CU receiving end and the D2D receiving end. In order to ensure normal cellular communication quality, the minimum rate requirement of CU is considered in the design scheme; The energy efficiency optimization problem with distributed antenna power and D2D transmission power as independent variables; using the KKT condition, the original multivariable optimization problem is transformed into a single variable optimization problem, and the closed-form solution of the optimal power is obtained; then a traversal method based on The power distribution algorithm of the algorithm is proved, and the effectiveness of the algorithm is proved; the whole method is simple, feasible and easy to implement.

The invention discloses an externally meshed gear pair which comprises two external gears. In polar coordinates, a single figure of the external gear I is sequentially composed of a cosine base compound curve which is composed of a line segment I, a cosine curve I, a line segment II and a cosine curve II. The line segment I, the cosine curve I, the line segment II and the cosine curve II meet first order continuity. A single conjugate figure of the external gear II corresponding to the single figure of the external gear I is obtained through a meshing principle and a conjugate gear pair is formed. A complete conjugate gear pair is obtained as the single figures of the external gear I and the external gear II respectively rotate around the center of the conjugate gear pair and evenly distributed according to the number of teeth of the conjugate gear pair. An asymmetric gear pump figure is obtained by adjusting symmetrical coefficients of the gear figure so that long sides in two gear sides of the gear are suitable for transmission, while short sides are suitable for seal. Separation of functions of transmission and seal are benefited and transmission and seal performance is enhanced. Fairness between the gear pairs can reach first order continuity and a mathematical model is set up for multivariate optimization of the gear pair.

The present invention is a full-time multi-optimized dispatching method of a power grid for improving wind power acceptance capacity, which belongs to the field of power system optimization dispatching. Thermal units, pumped storage systems, energy storage devices, electric boilers and intermittent loads, etc.; (2) According to the efficiency of the above-mentioned parts participating in wind power reception and the cooperation or contradiction between each part, formulate a coordinated optimal dispatching plan for the entire power grid Improve wind power acceptance capacity; (3) Due to the influence of seasonal factors, some power sources and load characteristics are quite different, and the capacity of wind power acceptance in different periods is different. Therefore, it is necessary to consider the characteristics of seasonal and environmental factors, and establish multiple optimization of the full-time power grid. Dispatch and accept wind power method; the invention comprehensively and cooperatively mobilizes each subsystem in the power grid to receive wind power, and formulates specific optimal dispatch strategies for different seasonal factors to improve the efficiency of the power grid.

A method for minimizing system energy consumption of non-orthogonal multiple access narrowband Internet of Things based on mobile edge computing, which is realized by jointly optimizing transmit power, computing resource allocation, and decoding sequencing in a narrowband Internet of Things network with both MEC and NOMA technologies System energy consumption minimization, the optimization problem is described as a multivariate optimization; P1 is decomposed into two sub-optimization problems: optimizing resource allocation and optimizing decoding sorting, wherein, for the optimization of resource allocation, firstly, according to the one-to-one logarithmic transformation Transform the P1 problem into a P2 convex problem, and then use the gradient descent algorithm to optimize the problem of transmission power and computing resource allocation; secondly, for the optimization of device decoding sorting, the tabu search algorithm is used to optimize variables. The present invention provides a method for minimizing energy consumption of the entire system by designing an optimization algorithm for joint transmission power, computing resource allocation, and device decoding and sorting in the narrowband Internet of Things with both MEC and NOMA technologies.

The invention discloses a conjugate gear pair based on a common rack, wherein each single tooth profile of the common rack is formed by a cosine-based composite curve composed of a straight line segment I, a cosine curve I, a straight line segment II and a cosine curve II in a rectangular coordinate system, and the straight line segment I, the cosine curve I, and the straight line segment II and the cosine curve II sequentially meet first-order continuous requirements. Each single conjugate tooth profile, corresponding to each single tooth profile of the common rack, of an inner gear or an outer gear is obtained through the meshing principle, and therefore the conjugate gear pair is formed. The whole conjugate gear pair is formed by the single tooth profiles, which are evenly distributed around the center of the inner gear or the outer gear according to the number of teeth of the inner gear or the outer gear, of the inner gear or the outer gear. According to the conjugate gear pair based on the common rack, through the adjustment of the symmetric factor of the tooth profiles, the non-symmetrical gear pair can be obtained, and therefore the long side in the two tooth sides is more suitable for transmission, the short side is more suitable for being used as a seal when the tooth profiles are used as a pump, the separation of a transmission function and a sealing function is benefited, the transmission performance and the sealing performance are enhanced, and a design method of the conjugate gear pair based on the common rack builds a mathematical model for the multivariate optimization of gear pairs.

The present invention provides a gas station recommendation method based on multivariable optimization. The method includes acquiring gas station recommendation indication information, and judging whether a gas station recommendation is required according to the gas station recommendation indication information; if a gas station recommendation is required, extracting All gas stations along the driving route constitute a gas station preparation set, each element in the gas station preparation set corresponds to a gas station, and the elements include attribute data of multiple dimensions; according to the preset method in the gas station preparation set Determine the candidate set; recommend each gas station in the candidate set to the user, and determine the target gas station according to the user's selection result. The present invention significantly improves the user's travel experience.

A method for maximizing the throughput of an energy-collecting wireless relay network based on the asynchronous dominant actor-critic algorithm, comprising the following steps: 1) in the energy-collecting wireless relay network, the maximum throughput is achieved through optimal management of renewable energy, wherein , the optimization problem is described as a multivariate optimization problem; 2) The problem P1 is decomposed into two parts of optimization: power sub-optimization and slot sub-optimization, that is, the variable p is optimized by the asynchronous dominant actor-critic algorithm i and to get the optimal r i . The invention provides a method for maximizing system benefits with maximum throughput through joint time scheduling and power allocation in an energy-collecting wireless relay network.

A method for maximizing the throughput of an energy-intensive wireless relay network based on deep deterministic policy gradients, comprising the following steps: 1) In the energy-intensive wireless relay network, the maximum throughput is achieved through optimal management of renewable energy, wherein, optimizing The problem is described as a multivariate optimization problem; 2) The problem P1 is decomposed into two parts of optimization: power sub-optimization and slot sub-optimization, that is, the deep deterministic policy gradient method optimizes the variable p i and to get the optimal r i . The invention provides a method for maximizing system benefits with maximum throughput through joint time scheduling and power allocation in an energy-collecting wireless relay network.

Disclosed is a cold-rolling mill multivariate strip shape control method based on a constrained optimization algorithm. The method includes the steps that a multivariate optimization model used for solving an optimal regulating quantity of each strip shape regulating mechanism is designed; the multivariate optimization model is converted into a standard form of an optimization problem; an initial searching point for solving the multivariate optimization problem of the standard form is designed; a searching mechanism in the direction of each coordinate axis is designed; searching is performed in the direction of each coordinate axis, and a candidate value of an optimal point is calculated; a searching termination criterion is designed; and a cold-rolling mill multivariate strip shape control system model is established. By means of the cold-rolling mill multivariate strip shape control method based on the constrained optimization algorithm, the multivariate strip shape optimization model can be converted into a series of univariate optimization problems, and the method has the advantages of being high in calculation efficiency, saving the storage space of a controller and being capable of achieving concurrent computation and the like, and is quite suitable for development of a cold-rolling strip shape control system with the multiple strip shape regulating mechanisms.

The invention discloses a robust resource allocation algorithm of a cognitive heterogeneous wireless network and belongs to the technical field of communication cognitive heterogeneous wireless networks. The robust resource allocation algorithm is characterized by considering a robust resource allocation algorithm in a cognitive heterogeneous multi-network environment influenced by channel parameter perturbation, considering cross-layer interference constraint and bounded channel uncertainty and interference power uncertainty in an objective function, converting the original infinite dimensional multivariate optimization problem into the convex optimization problem of certainty by using a robust optimization theory, obtaining a robust power control algorithm by using a lagrangian duality method, and achieving sharing of the robust resource of the cognitive heterogeneous wireless network. Simulation results show that the algorithm can effectively protect the performance of users of a macro-cellular network and has stronger robustness.