61 results about "Sequential decision" patented technology

A system and method for sequential decision-making for customer relationship management includes providing customer data including stimulus-response history data, and automatically generating actionable rules based on the customer data. Further, automatically generating actionable rules may include estimating a value function using reinforcement learning.

This invention introduces a method, system and computer readable media that enables a computer system or computer networked system to perform enterprise portfolio transition optimization. The invention considers several key factors that are traditionally treated informally. The invention provides a sequential decision model for enterprise portfolio transition management of computer and software resources. Using a finite state Markov Decision process, a mathematical formulation is developed to ensure an optimal enterprise portfolio transition plan, with the objective of balancing expected value and risk, that is solved numerically via an approximate dynamic programming algorithm. The output of the model is a set of recommendations in the form of a transition plan for each of the computer and software resources selected within the enterprise portfolio during each phase of a planning horizon.

Computer software for and a method of determining location of a target image within a two-dimensional input image by employing a three-dimensional reference image comprising determining object edge points in the input image; given estimated seeker line-of-sight orientation and range data, computing a two-dimensional reference image from the three-dimensional reference image; applying a weighted-direction-cross-product Hough Transform to the object edge points and points of the two-dimensional reference image; classifying the input image as containing a target image or not; if the image contains a target image, identifying a location of the target image within the two-dimensional input image; computing confidence measure as to the selected location of the target image; and in the case of sequential decision, also performing confidence accumulation through multi-frames or multi-looks.

The invention relates to an extra-high-voltage alternating current and direct current transmission mode applicability selecting method and a device using the same. The method includes the steps: firstly, determining index parameters of an extra-high-voltage direct current transmission mode; secondly, establishing alternative schemes for extra-high-voltage transmission modes according to a power transmission scale and establishing a simulation model according to power transmission requirements and conditions of a power grid system; thirdly, calculating the index parameters in the alternative schemes in the second step according to the simulation model; fourthly, determining integrated weight according to the calculated index parameters in the alternative schemes in the third step; and fifthly, performing comprehensive optimization according to the determined index parameters in the alternative schemes in the fourth step based on fuzzy optimization to determine the optical transmission scheme in the alternative schemes. A penalty function is brought into an index calculation process, the requirement of indexes on safety feature can be more effectively met, the problem of multi-objective decision is converted into the problem of sequential decision for a unit system in a multilevel multi-objective level model, the problem of engineering evaluation is theorized, and analysis results are visual and persuasive.

Smart in-vehicle decision support system has been developed to address current challenges and offers a new approach to make right stop / go decisions for vehicles to drive through a signalized intersection. The methods and systems described herein exploit a novel conceptualization of the decision support problem as an integration process, where a decision support model takes advantages of vehicle-to-infrastructure communications and fuses the inputs from vehicles and intersection, which comprise key information of vehicle motion, vehicle-driver characteristics, signal phase and timing, intersection geometry and topology, and the definitions of red-light running, to explore a more complete variable space of physical and behavioral information and provide safer and more efficient decision supports to vehicles driving through a signalized intersection than the previous methods and systems. The novel formulation of the decision support model as a probabilistic sequential decision making process incorporates a set of decision rules that are responsible for different situations into the present invention, which enables each decision rule to quickly make a right decision and better improves both traffic safety and intersection throughput than the other existing formulations.

The embodiment of the invention discloses a method and a device for allocating resources for a wireless terminal directly connected user (D2D). The method comprises the following steps of: formulatinga reinforcement learning (RL) algorithm according to the uncertainty of a wireless channel state and a state transition possibility. Each pair of D2D users can be viewed as an independent agent, making decisions based on the channel state it observes locally. In order to solve the problem of sequential decision-making in multi-user systems, a multi-agent RL algorithm is proposed. We assume that D2D users do not have any information about the availability and quality of the resource blocks to be allocated, so the problem is modeled as a stochastic non-cooperative game. Therefore, each agent becomes a participant in the non-cooperative game, and all participants make decisions together in order to achieve the optimization of all benefits. The embodiment of the invention can lighten the loadand improve the capacity of the cellular network, and the system throughput can be maximized through effective energy management.

The invention relates to an optimal power flow calculation method considering discrete and sequential decision variables for a large-scale power distribution network. The optimal power flow calculation method is characterized by comprising the following steps of building a mathematical model of a complicated three-phase unbalanced power distribution network, carrying out complicated optimal power flow calculation decomposition, and carrying out a simplification process on network loss calculation of the three-phase unbalance power distribution network. By the optimal power flow calculation method, the complicated optimization problem can be decomposed into an integral linear programming primal problem and a non-linear feasible subproblem, decision making optimization and system security constraint are organically combined by using mutually-iterated calculation decisions, the optimal power flow is embedded to a decision process, and meanwhile, rapid calculation on asymmetric power flows at a large scale is achieved. The optimal power flow calculation method is suitably used for three-phase unbalance power flow calculation, looped network structure, bidirectional power flows, real-time decision of discrete and sequential decision and optimal power flow, and has the advantages of scientific reasonableness, high adaptability, operation accuracy, high speed and the like.

A system and method for defining and calibrating the inputs to a sequential decision problem using historical data, where the user provides historical data and the system and method forms the historical data (along with other inputs) into at least one of the states, actions, rewards or transitions used in composing and solving the sequential decision problem.

A method for order prioritization includes calculating a cycle time for a product order of a plurality of product orders using an artificial neural network, determining a first order priority of the product order based on a priority index using an analytic hierarchy process, determining a second order priority of the product order based on event based simulation model, and determining a shipping date for the product order based on the second order priority. The artificial neural network calculates the cycle time based upon product order type and a plurality of component counts. The analytic hierarchy process determines a first order priority based upon a plurality of product order attributes. The simulation model determines a second order priority and completion time based upon the first order priority, product model, product type, a plurality of component counts, manufacturing capacity and inventory data, and production time data for historical product orders.

The invention provides an aero-engine remaining service life estimation method based on deep reinforcement learning, and the method comprises the steps: taking the requirements and characteristics ofaero-engine life estimation as a sequential decision problem, and establishing a corresponding Markov decision process model; for the Markov model, formulating interaction rules such as state action rewarding and the like, designing a life estimation rewarding function, and providing a deep learning model of a life estimation strategy according to the characteristics of aero-engine life estimationdata; and finally, learning an optimal aero-engine life estimation strategy in the Markov model by using a deep reinforcement learning algorithm. According to the method, the over-fitting risk brought by a conventional supervised learning method is overcome, a better aero-engine service life estimation strategy can be obtained, the accuracy of aero-engine service life estimation can be improved,the timeliness of maintenance according to conditions is improved, the safety of aircraft flight is enhanced, and unnecessary maintenance cost is saved.

The invention discloses a container-based edge computing resource allocation method and system for the Industrial Internet of Things. The method is as follows: according to the data types collected by the perception layer, the tasks are divided into n types, and through historical data statistics, it is obtained that the tasks arrive at the edge successively Probability relationship on the server cluster; according to the system state space and decision space, allocate computing resources to real-time tasks and make sequential decisions; according to the resource allocation algorithm based on reinforcement learning, the system allocates resources from low, medium and high according to the current state Select the most reasonable strategy among the strategies to allocate resources to the current task; build a task scheduling processing model, create a container, and then schedule data to the container for processing and analysis. After the task is completed, delete the container to complete the allocation. In the case of limited resources, the present invention allocates different types of data computing resources, creates containers for processing and analysis, and improves resource utilization efficiency and the total rate of task processing. The invention is applicable to the field of data processing of the industrial internet of things.