497 results about "Combinatorial optimization" patented technology

Apparatus and methods are disclosed for simultaneously tracking multiple finger and palm contacts as hands approach, touch, and slide across a proximity-sensing. compliant, and flexible multi-touch surface. The surface consists of compressible cushion, dielectric, electrode, and circuitry layers. A simple proximity transduction circuit is placed under each electrode to maximize signal-to-noise ratio and to reduce wiring complexity. Such distributed transduction circuitry is economical for large surfaces when implemented with thin-film transistor techniques. Scanning and signal offset removal on an electrode array produces low-noise proximity images. Segmentation processing of each proximity image constructs a group of electrodes corresponding to each distinguishable contact and extracts shape, position and surface proximity features for each group. Groups in successive images which correspond to the same hand contact are linked by a persistent path tracker which also detects individual contact touchdown and liftoff. Combinatorial optimization modules associate each contact's path with a particular fingertip, thumb, or palm of either hand on the basis of biomechanical constraints and contact features. Classification of intuitive hand configurations and motions enables unprecedented integration of typing, resting, pointing, scrolling, 3D manipulation, and handwriting into a versatile, ergonomic computer input device.

A method and system for dynamic combinatorial auctions employing bid composition restrictions is disclosed. Three exemplary types of bid composition restrictions, which may be applied either singly or in combination, are disclosed: non-additive activity restrictions, subset restrictions, and superset restrictions. These restrictions produce more efficient auction outcomes by placing extra constraints on bidders who specify larger combinations in their bids, thereby penalizing attempts at strategic bidding without ruling out genuine attempts to avoid an exposure problem.

Route planning methods for use by a package delivery service provider are disclosed that satisfy a stochastic daily demand while taking advantage of drivers' route familiarity over time. A model for estimating the value of driver familiarity is disclosed along with both an empirical and a mathematical model for estimating the value of route consistency, along with a Core Area Route Design which involves the concepts of combinational optimization, meta-heuristic algorithms, tabu search heuristics, network formulation modeling, and multi-stage graph modeling. In one embodiment, a service territory is divided into unassigned cells associated with a grid segment involving prior driver delivery stops, and a driver from a pool of unassigned drivers is assigned to a route based on examining each driver's grid segment visiting frequency limit with respect to a minimum limit so as to optimize driver selection based on of each driver's familiarity with the route.

The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem in a space, the method comprising: generating a set of solutions of portfolio allocations in the space, the space having a plurality of dimensions; selecting a first dimension from the plurality of dimensions, the first dimension being a dimension under consideration; dividing the space into bins based on each dimension in the plurality of dimensions other than the dimension under consideration; determining a respective point in each bin with the most extreme value in the dimension under consideration; determining, based on the point in each bin with the most extreme value, whether other points in the space are dominant or dominated; and removing the dominated points from further consideration, so as to result in a reduced set of solutions, the reduced set of solutions being used in investment decisioning.

The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method sequentially comprising: generating a non-dominated solution set in a space; applying a first set of user-specified constraints to reduce the solutions in the non-dominated solution set to a solution subset; and executing a series of local tradeoffs on the solution subset to result in a resulting solution subset, the local tradeoffs being performed in a lower dimension performance space as compared to the space, and the solution subset being used in investment decisioning.

Screening of a library of particles in vivo and/or in vitro using Polyplex Iterative Combinatorial Optimization (PICO) allows for the design of particles for targeting a specific organ, tissue (e.g., cancer), or cell. Particles may, for example, include different targeting agents (e.g., aptamers or plurality of aptamers) on their surfaces, and the aptamer or aptamers may be evolved to provide better targeting of the particles. Libraries of particles are enriched in characteristics of particles that have been found to migrate to a tissue of interest, be taken up by cells, etc. The process may be repeated to engineer particles of a desired specificity or biological function.

The invention discloses an optimized operation control method and system of a distributed energy system. The method include: S1, collecting environmental information and actual operation data of a unit so as to acquire a change rule of cold and hot load of a distributed energy station user with season and moment, and establishing a cold, hot and electric load prediction model; S2, optimizing the cold, hot and electric load prediction model on line by introducing real-time calibration factors and the actual operation data of the unit; S3, on the premise that the energy utilization efficiency is met, establishing a dynamic optimized load distribution model according to the dynamic requirements of the predicated cold, hot and electric load by taking a whole-plant economic benefit optimization as an objective, and outputting dynamic optimized load distribution results; S4, based on the whole-plant economic benefit optimization, establishing an optimal combination model according to the dynamic optimized load distribution results, and outputting a unit operation optimization command. High-precision load prediction information can be acquired, a corresponding optimization command is formed, and online optimization control is performed on the load dynamics and unit operation.

A CHP optimal dispatching model is a mixed integer programming model and is used for a district heating system (DHS) comprising a heat source, a heating network and a heat load, and the heating network comprises a heat transmission network and a heat distribution network. A plurality of heating areas is divided, and one day is divided into a plurality of time periods; the heat transmission loss of the heat distribution network is omitted, and a heat transmission network model taking transmission time delay of the heating network into consideration is established according to the heat transmission network; a terminal heat consumer model capable of reflecting indoor temperature is established; and a combined optimal dispatching model comprising conventional generators, wind power units, CHP units, electric boilers and heat storage tanks is established.

A method and computer system for conducting a progressive, price-driven combinatorial auction wherein equilibrium prices supporting optimal allocation are provided. The auction mechanism allows bids on bundles and uses an innovative price setting mechanism that ensures that the bundle prices reported by the auction have the property that each participant's or bidder's tentative allocation maximizes the value reported in the participant's bid. The auction is progressive, and requires that a participant's new bid improves his previous bid in a precisely defined manner. The auction terminates when no participant submits a new bid, or according to some other specified criterion.

The invention discloses a self-adaptive composite service optimization method based on multi-agent reinforced learning. The method combines conceptions of the reinforced learning and agents, and defines the state set of reinforced learning to be the precondition and postcondition of the service, and the action set to be the Web service; parameters for Q learning including the learning rate, discount factors and Q value in reinforced learning are initialized; each agent is used for performing one composite optimizing task, and can perceive the current state, and select the optimal action under the current state as per the action selection strategy; the Q value is calculated and updated as per the Q learning algorithm; before the Q value is converged, the next round learning is performed after one learning round is finished, and finally the optimal strategy is obtained. According to the method, the corresponding self-adaptive action strategy is worked out on line as per the environment change at the time, so that higher flexibility, self-adaptability and practical value are realized.

The invention notably relates to a computer-implemented method for reconstructing a 3D modeled object that represents a real object, from a 3D mesh and measured data representative of the real object, the method comprising providing a set of deformation modes; determining a composition of the deformation modes which optimizes a program that rewards fit between the 3D mesh as deformed by the composition and the measured data, and that further rewards sparsity of the deformation modes involved in the determined composition; and applying the composition to the 3D mesh. The method improves reconstructing a 3D modeled object that represents a real object.

Improved composite armor designs use optimally shaped ceramic pellets and a web system for patterning the pellets, improving manufacturability, and providing additional structural reinforcement. The result is lightweight, composite hybrid structures for ballistic protection particularly suited to tactical ground vehicles. The preferred embodiment is a combination of three major components: 1) an optimally designed web system that allows armor tiles to be attached to it and that can be easily integrated with existing vehicle structures; 2) an advanced composite armor unit using a patent-pending BTR (Bio-mimetic Tendon-Reinforced) material as the supporting structure; and 3) optimally placed “waiting materials” which can provide enhanced ballistic impact resistance, energy absorption capability and structural integrity. These “waiting materials” are structural members that are not active at the beginning of the ballistic impact, but become active when needed or the active members have failed.

The present invention discloses a helicopter aerodynamic layout optimization method capable of reducing the adverse effect of aerodynamic interference. According to the method, aiming at the defects of a wind tunnel test and a CFD method and based on a discrete vortex method and a surface element method, a coupled aerodynamic interference high-precision analysis model for a helicopter is set up, so that the influence of aerodynamic interference on the helicopter can be considered only by simulating calculation without carrying out the wind tunnel test; compared with the CFD method, the method has the advantages of greatly reducing the requirements of a simulation model for computing resources and shortening computing time. In order to further reduce calculated amount and a optimization region, a helicopter fuselage shape parameter model is set up by a parametric method and a combined optimization method based on an agent model is adopted, so that the optimal helicopter aerodynamic outline and layout are obtained and the purpose of reducing aerodynamic interference to improve the flight characteristics of the helicopter is achieved. The helicopter aerodynamic layout optimization method can be applied to the conceptual design phase of the helicopter so as to improve the flight characteristics, the balancing characteristic, stability and manoeuvrability of the helicopter.

The invention belongs to the field of power systems, in particular to a block supply-based distribution network planning design method. The method comprises the following steps of: adopting a typical power supply mode for defining main components and key points of a power network structure, a power supply unit, power network equipment and other power supply systems, and dividing a planning area into related, influenced and independent power supply regions in different levels according to functions, geographic orientation, administrative division and other conditions; adopting a planning design sequence from bottom to top to formulate a distribution network planning design scheme for each power supply region from the starting point of social and economic development and power demands in each power supply region; and finally forming a distribution network planning design scheme of the planning area through combined optimization. The block supply-based distribution network planning design method can formulate distribution points of the power network structure, transformer substations or power distribution stations, also can formulate a development program of each power supply unit and main equipment types of the power network, and is a distribution network planning design method which integrates planning and design and expands the conventional distribution network planning boundary.

The invention discloses a method for calculating the optimal admitting ability for wind power multipoint access of a regional power grid and belongs to the technical field of wind power penetration methods of the power grid. The method for calculating the optimal admitting ability for the wind power multipoint access of the regional power grid comprises the steps that the installed capacity of all wind power plants is initialized, and data such as the local wind speed are acquired; the output condition of all the wind power plants is calculated, and unit commitment and optimization within all dispatching cycles are conducted, so that an annual unit commitment mode of an electric power system comprising the wind power plants is obtained; power flow calculation is conducted based on each period of time, whether an operation constraint condition of the system is met is verified, and a final objective function value is calculated after simulation is conducted on the system for more than 8760 hours; the installed capacity of the wind power plants is updated, optimized iteration is conducted on an objective function, and optimal solution of the wind power generating capacity is achieved, namely the optimal admitting ability of the power grid which enables the economical efficiency of the system to be optimized on the premise that the safety and stability constraint conditions are met is achieved. According to the method for calculating the optimal admitting ability for the wind power multipoint access of the regional power grid, an optimal wind power admitting capacity calculation model with the largest net earning of a wind power plant and power grid combination system (with the environmental benefit considered) as the purpose is established, and suggestions can be provided for the proper establishment of the installed capacity of the regional wind power plants.

The invention belongs to the technical field of power system control and relates to a multi-target unit combination intelligent optimization system based on power network state identification. The multi-target unit combination intelligent optimization system is an intelligent dynamic modeling model formed by an optimization calculation module and a power network parameter acquisition module, optimization calculation is related to awaiting distribution amount introduction and distribution result derivation, the power network parameter acquisition module is connected with an energy control system, and the energy control system supplies data to the power network parameter acquisition module. The multi-target unit combination intelligent optimization system based on power network state identification is an improvement of the traditional unit combination optimization mode, automatically identifies a power network real-time operation state by utilizing a knowledge base system and is automatically configured with a multi-target optimization unit combination model according to a certain regulation based on the power network operation state. Starting from power dispatch reality, the invention converts various target optimization problems to optimization sequencing problems based on different regulations of each unit in a feasible range, thereby conveniently dispatching results of observation, explanation and evaluation optimization calculation of decision making persons.

The invention belongs to the technical field of cross-media information retrieval and particularly relates to face and name aligning method and system based on image characteristics and text content in cross media news retrieval. In the invention, four main algorithms are included, and are name importance assessment algorithm, multimode information discovery algorithm based on web excavation, face set cohesion algorithm and multimode aligning combination optimization algorithm. In the invention, the related image characteristics and text content processing method is used, meanwhile, relative mathematical model is built, optimization to new picture search is performed, and through multi-grade and deep-level text content analyses and effective face-name alignment evaluation mechanism, and combination optimization at the aim of problems can be achieved. According to the invention, a great significance to efficient image retrieval performed under the consideration of high level semantic information of images and on the basis of large-scale and multifarious new image can be played, the retrieval relativity can be enhanced, the user experience is enhanced, and the wide application value is played in the field of medium information retrieval.

The invention relates to a multi-parameter time-varying robot spraying method which comprises the following steps: firstly, establishing a free-form surface multivariable spraying model which uses flow of a spraying gun, a spraying distance and a spraying speed as model independent variables; then discretizing an original spraying path into a plurality of sections of minute sub paths, distributing initial values of time-varying spraying process parameters to each sub path and on the basis of the established multivariable spraying model, predicting distribution of an initial coating thickness; next, on the basis of a prediction result on distribution of the coating thickness, carrying out combined optimization on the time-varying process parameters to obtain the optimal process parameters on each section of sub path and finally, obtaining a multi-parameter time-varying spraying path of a surface to be sprayed. According to the method disclosed by the invention, various process parameters are used as variables; by the multivariable spraying model and a free-form surface coating thickness predicting method, the optimal process parameters on the sub paths of the discretized spraying path are obtained; dynamic optimization of the process parameters is implemented; the multi-parameter time-varying robot spraying method has the important effect of improving spraying operation efficiency, quality and safety of a robot.

The invention discloses intelligent logistics distribution and delivery based on a discrete particle swarm optimization algorithm and aims at scheduling the path of a transportation vehicle so as to save the transportation cost. A coding mode and an operator based on the set and the probability are introduced on the basis of a framework of a standard particle swarm optimization algorithm, the particle swarm optimization algorithm which is originally suitable for a continuous space can be introduced into a discrete combined optimized space, so that the problem that the path of the vehicle is scheduled is solved and the advantages of high operation efficiency, strong optimization capacity, strong robustness, and the like endowed by the traditional particle swarm algorithm can be maintained. In addition, by using heuristic information to construct the positions of particles and introduce a local operator search, features of the problem and information contained in the data are utilized, and thereby the solving result of the algorithm is further enhanced. By adopting normalization weighting and decision idea to deal with the target, the transportation path is strived to be shortest while the number of transportation vehicles are required to be minimum, therefore, the transportation cost of logistics distribution and delivery businessmen can be reduced to the maximum extent.