2339 results about "Multi-objective optimization" patented technology

The invention provides control systems and methodologies for controlling a process having one or more motorized pumps and associated motor drives, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

Control systems and methodologies are disclosed for controlling a process having one or more motorized pumps and an associated motor drives, which comprise selecting a desired operating point within an allowable range of operation about a process setpoint according to performance characteristics associated with a plurality of components in the process, and controlling the system according to the desired operating point. The control system comprises a motor drive providing electrical power to a motor in a controlled fashion according to a control signal, and a controller providing the control signal to the motor drive according to a desired operating point within an allowable range of operation about a process setpoint. The controller selects the desired operating point according to one or more performance characteristics or criteria associated with a plurality of components in the process, such as cost, throughput, lifetime, or the like. Also disclosed are methods and systems for diagnosing operating conditions in a pump or other motorized device.

The invention provides a deep learning adversarial attack defense method based on a generative adversarial network. The method comprises the following steps: step 1), based on the high performance ofthe generative adversarial network in learning sample distribution, designing a method for generating an adversarial example through the generative adversarial network, and after adding a target modelnetwork set TMi, enabling the sample generation based on a G network to become a multi-objective optimization problem; and the training for an AG-GAN model is mainly for the parameter training of thegenerative network G and a discrimination network D, and is divided into three modules; and step 2), using the adversarial example generated by the AG-GAN to train an attacked deep learning model, soas to improve the capability of the deep learning model of defending different types of adversarial examples. The deep learning adversarial attack defense method based on the generative adversarial network provided by the invention effectively improves the security.

An apparatus and method for automatically generating radiation treatment planning parameters are disclosed. In accordance with the illustrative embodiment, a database is constructed that stores: (i) patient data and past treatment plans by expert human planners for these patients, and (ii) optimal treatment plans that are generated using multi-objective optimization and Pareto front search and that represent the best tradeoff opportunities of the patient case, and a predictive model (e.g., a neural network, a decision tree, a support vector machine [SVM], etc.) is then trained via a learning algorithm on a plurality of input/output mappings derived from the contents of the database. During training, the predictive model is trained to identify and infer patterns in the treatment plan data through a process of generalization. Once trained, the predictive model can then be used to automatically generate radiation treatment planning parameters for new patients.

Agent-based models (ABMs)/multi-agent systems (MASs) are one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the global system dynamics, usually measured by different conflicting criteria. One problem that can emerge is that of tuning the controllable system parameters at the local level, in order to reach some desirable global behavior. According to one exemplary embodiment t of the present invention, the tuning of an ABM for emergency response planning can be cast as a multi-objective optimization problem (MOOP). Further, the use of multi-objective evolutionary algorithms (MOEAs) and procedures for exploration and optimization of the resultant search space can be utilized. It is possible to employ conventional MOEAs, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES), and their performance can be tested for different pairs of objectives for plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives can be seen. Additional robustness analysis may be performed to assist policy-makers in selecting a plan according to higher-level information or criteria which is likely not present in the original problem description.

The invention discloses a micro-grid planning and capacity allocation method based on multi-objective optimization. The method is characterized in that the method comprises the steps of (1) setting the operation mode of a micro-grid, wherein the operation mode of the micro-grid comprises an independent mode and a grid-connected mode; (2) inputting basic data which comprise the system condition, electrovalence parameters, load parameters, photovoltaic parameters, wind electricity parameters and storage battery parameters; (3) pre-processing the basic data; (4) optimizing a distributed power supply and an energy storage system. According to the micro-grid planning and capacity allocation method, distributed power supply capacity and energy storage system capacity in micro-grid planning can be solved jointly, and optimizing allocation can be carried out at the same time.

The invention discloses a micro-grid multi-objective optimized operation control method, which comprises the steps of: constructing a micro-grid operation indicator function aiming at wind-light complementary and cold, heat and electricity combined supply micro-grid; then constructing a complete small signal model of the micro-grid for theoretical analysis to provide theoretical guidance for the multi-objective optimized operation control of the micro-grid; and finally on the basis of a multi-objective intelligent optimization theory, comprehensively using power system computer aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) and MATLAB software to design the micro-grid multi-objective optimized operation control method, so as to enable the micro-grid to realize comprehensive optimization of multiple operation indicators such as economy, cleanness, reliability, stability, dynamic performance and the like in different operation modes such as a synchronized operation mode, a mode capable of being switched between the synchronized operation mode and an independent operation mode, an independent operation mode with sudden load change and a resynchronized operation mode.

The invention discloses a method for optimally designing a machine tool body structure, which comprises the following steps of: carrying out multiobjective optimization on wall thickness of a machine tool body and physical dimension of a rib plate; and carrying out comprehensive optimization analysis on the structure of the machine tool body, wherein the step of carrying out the multiobjective optimization on the wall thickness of the machine tool body and the physical dimension of the rib plate comprises the procedures of: establishing a machine tool body parameterized model; determining a boundary condition; determining a design variable, a restrict condition and a target function, establishing an optimization design model; and correcting the wall thickness of the machine tool body and the dimension of the rib plate; and the step of carrying out the comprehensive optimization analysis on the structure of the machine tool body comprises the procedures of: establishing a geometrical model of the machine tool body; determining a boundary condition; determining a topological optimization target, establishing a topological optimization model; and correcting the structure of the machine tool body. According to the invention, the traditional optimization design method of the machine tool body is changed, movable and static rigidity characteristics are improved, and the manufacture cost is lowered. The invention can be widely applied to the optimization designs of various machine tool support member structures.

The invention relates to a multi-objective coordination-typed self-adaptive cruise control method for a vehicle, comprising the following steps: 1) according to the detail requirements of the multi-objective coordination-typed self-adaptive cruise control for a vehicle, the performance indicators and I/O restriction of MTC ACC are designed, and multi-objective optimization control problem is established; and 2) MTC ACC control law rolling time domain is used for solving the objective optimal control problem, and the optimal open-loop control quantity is used for carrying out feedback and achieving closed-loop control. Based on the steps, the control method comprises the following four parts of contents: 1. the modeling for the longitudinal dynamics of a traction system; 2. the performance indicators of MTC ACC; 3. the I/O restriction design of MTC ACC; and 4. solution by the MTC ACC control law rolling time domain. By constructing multi-objective optimization problem, the control method not only solves the contradiction among the fuel economy, the track performance and the feeling of the driver, moreover, on the same simulation conditions, compared with the LQ ACC control, the control method simultaneously reduces the fuel consumption and vehicle tracking error of the vehicle, and achieves the multi-objective coordinating control function.

The invention provides an optimization method for locating and sizing of a distributed power. The method comprises the following steps: establishing a multi-objective optimization model of the locating and sizing of the distributed power; defining constrains of the multi-objective optimization model; establishing a distributed power random output model and processing the distributed power random output model; establishing a load random output model; and carrying out locating and sizing of distributed power. According to the invention, with minimum network loss and maximum delayed investment benefits being as target optimization functions and by utilizing a two-step optimization method, the access location of the distributed power in the power distribution network and installation capacity are obtained, so that the positive effect of the access of the distributed power on the power distribution network is fully embodied and the optimization method can be used to evaluate economic benefit of the access of the distributed power to the power distribution network from the aspect of capacity-expanding effect.

The invention relates to an individuation catering recommendation method and system based on multiple targets. The individuation catering recommendation method mainly includes the steps that essential information and behavior information (including diet records and browsing behaviors on webpages) of a user are collected and recorded into a database; a user model is built according to user information in the database, and individuation nutrition recipes are recommended; nutrition balanced diet recipes are generated through multi-target optimization catering models, the similarity between the recipes based on nutrient elements is calculated through a collaborative filtering recommendation model and an equivalence interchange model, various interchange recipe lists are generated from recipe bases, and the dietary structure is enriched; the nutrition balance of the recipes is detected, improvement measures are provided by comparing practical contents and recommended nutrient intakes of various nutrients in the generated recipes, and the designed recipes trend to be reasonable.

The invention relates to an image description generation method based on a depth LSTM network, comprising the following steps: (1) extracting the CNN characteristics of an image in an image description dataset, and acquiring an embedded vector corresponding to the image and describing the words in a reference sentence; (2) building a double-layer LSTM network, and carrying out series modeling based on the double-layer LSTM network and a CNN network to generate a multimodal LSTM model; (3) training the multimodal LSTM model by means of joint training; (4) gradually increasing the number of layers of the LSTM network in the multimodal LSTM model, carrying out training each time one layer is added to the LSTM network, and finally, getting a gradual multi-objective optimization and multilayer probability fused image description model; and (5) fusing the probability scores output by the branches of the multilayer LSTM network in the gradual multi-objective optimization and multilayer probability fused image description model, and outputting the word corresponding to the maximum probability through common decision. Compared with the prior art, the method has such advantages as multiple layers, improved expression ability, effective updating, and high accuracy.

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm. The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition. Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information. The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization. A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected. The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases.

The invention discloses a network text segmenting method based on the genetic algorithm, used for segmenting short network texts. The method comprises the following steps of: evaluating a Latent Dirichlet allocation (LDA) model corresponding to a corpus by using a Gibbs sampling method, inferring latent topic information using the model, representing texts by using the latent topic information; then transforming a text-segmenting process into a multi-target optimum process by using a parallel genetic algorithm, and calculating the coherency of segmented units, the divergence among the segmented units and fitness functions by using deeper semantic information; and carrying out the genetic iteration of the text segmenting process, and determining whether the segmenting process terminates based on the similarity among multi-iteration results or the upper limit of iterations to obtain the global optimal solution for segmenting the texts. Therefore, the invention improves the accuracy for segmenting the short network texts.

The invention discloses a cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm. The method comprises the following steps that s11, the basic information data of a cascade reservoir system are acquired; s12, a multi-objective scheduling model including power generation, estuarine ecology and water supply is established according to the information of the reservoir system; and s13, the optimal scheduling scheme of the cascade reservoir system is solved by performing the improved artificial bee colony algorithm. Global optimization of the reservoir scheduling problem can be realized so that the calculation efficiency and accuracy can be enhanced, and a new approach can be provided for solving the multi-objective optimization scheduling problem of the cascade reservoir system.

The invention belongs to the field of path planning technology and specifically relates to a three-dimensional multi-UAV coordinated path planning method based on SAS. The method comprises the following steps: carrying out modeling on a path planning environment; initializing multi-target SAS calculation parameters consisting of the length of a minimum path section, a maximum turning angle, a maximum angle of climb/glide, a minimum safe distance of UAVs and a minimum flight altitude of UAVs; initializing the positions of the UAVs, wherein each UAV represents a path; updating the positions of the UAVs; expanding a current node; determining whether a path section collides with other path sections; updating a node table of the path section; executing a step (8) if minimum path cost set in the step (2) is met, and otherwise, executing the step (3); determining a cooperated planned optimal path so as to complete path planning. The method overcomes multi-objective optimization problems, has versatility, provides a reasonable optimal solution for a decision maker and better accords with practical needs.

A multiobjective management system for saturated traffic road networks comprising: green wave coordination of locally adaptive traffic control units, traffic movement optimization and live traffic route guidance. Current traffic congestion measurements on intersections are generated from local traffic cameras and remote air-borne conventional cameras and thermal sensing imaging cameras or satellite radar such as SAR/ISAR using optical image brightness analysis. At the first stage of traffic optimization, individual local intersection green times are computed based on current traffic congestion level. At the second stage optimization, the central traffic server uses a multiobjective approach to coordinate the current locally-optimized green times of the first stage and create input constraints for green-way coordination of plurality of traffic lights. The server updates dynamically current cycle start and green times on all network-connected traffic light controllers and also broadcasts recommended travel times, green times and green waves to all on-line client vehicle navigation units. Traffic server and individual client guidance units utilize novel time-dependent modifications of an A*-type algorithm to update current travel and recommended travel times and to execute fastest route searches.

The invention discloses a combined cold heat and power supply microgrid multi-objective dynamic optimal operation method; characteristics of translatable electrical load are firstly considered in an optimization process, then schedulability of source side and energy storage system are considered, contribution in each period in three kinds of controllable units serves as optimization variables, minimum system operation cost and minimum pollutant emission control expense serve as optimal operation targets, and a mathematical model of current multi-objective optimal operation problem is established; an excellent particles leading multi-objective particle swarm optimization algorithm is adopted to solve the optimization problem, that is, a single objective genetic algorithm is utilized to respectively find two points including minimum system operation cost and minimum pollutant emission control expense, and the two points serving as excellent particles is utilized to lead an optimal direction of the multi-objective particle swarm algorithm; the invention provides an effective multi-objective dynamic optimal operation method, and the method is significant for improving energy source comprehensive utilization efficiency of a multiple energy coupled system and promoting renewable energy source development.

The invention discloses a distributed power source contained power system multi-target reactive-power optimization method in the field of power system reactive-power optimization. The technical scheme includes: 1, deducing a model of a wind-driven generator in power flow calculation; 2, initializing power grid parameters and grid-connected parameters of a distributed power source; 3, constructing an individual vector formed by system reactive-power optimization control variables, and initializing species; 4, performing the power flow calculation according to the initialized species and grid parameters after grid-connection of the distributed power source, and calculating objective function values; 5, performing multi-target optimization by utilizing the harmony search hybrid algorithm based on artificial bee colony; and 6, finishing the optimization process and outputting optimized results. The distributed power source contained power system multi-target reactive-power optimization method is a hybrid optimization algorithm ABS-HS which integrates the advantages of global search of the artificial bee colony (ABC) algorithm with local search of the existing harmony search (HS) algorithm, so that efficiency and robustness of the algorithm are improved.