515 results about "Optimal decision" patented technology

This invention relates to a method for optimizing the cost of interaction with a caller utilizing an automated interactive voice response system. A plurality of interactions between a caller and an automated interactive voice response system are analyzed. Discrete attributes of the interactions between the callers and the automated interactive voice response system are analyzed, and a set of logical statements relative to the discrete attributes is formulated. The set of logical statements is applied to the interaction with the caller, from which an action is determined.

This invention relates to a method for implementing an off shore / off site activity in an organization, with most optimal skills transfer process. The said process includes the steps ofassessing the suitability of outsourcing and whether the activity should be carried out on-site, locally or remotely, based on pre-determined parameters and decision ratios,planning the process including the minimum groundwork required before the actual process can begin,identifying the various milestones in the process and the deliverables in each milestonesmonitoring the progress of the migration process and the ways and means to take corrective action, and,evaluating the results of the development.

The invention discloses a multi-time scale forecasting method for a road traffic running situation. Highway traffic parameters in different time scales are analyzed according to the running time-space characteristics of highway traffic flow; the highway road traffic running situations in different time scales are forecast by an exponential smoothing algorithm, a weighted average algorithm and a Kalman filtering method respectively; a highway road traffic running situation evaluation index system and a multi-time scale highway traffic flow running situation forecasting technology are constructed to implement the conversion from experience guide to science guide for the highway running management and the preliminary conversion from passive management to active management. Therefore, the running efficiency of a road traffic running situation forecasting system can be increased effectively, the running cost of the system is reduced, the coordination degree between road traffic guidance and management can be improved obviously, and an optimal policy is provided for improving a traffic management and control measure and planning a travel plan for a road traffic manager and a user to a large extent.

A method for displaying information related to a chaotic event. A mathematical optimization algorithm is used to select an optimal decision set for a user. The mathematical optimization algorithm takes as input a decision template, chaotic event information regarding a chaotic event, and a user profile. The optimal decision set is displayed for the user.

A method for displaying information related to a chaotic event. A mathematical optimization algorithm is used to select a first optimal decision set for a user. The mathematical optimization algorithm takes as input a decision template, chaotic event information, at least one constraint, and a user profile. A heuristic algorithm is used to eliminate a first subset of decisions. The first subset of decisions is in the first optimal decision set. A second optimal decision set is formed. The second optimal decision set comprises the first optimal decision set less the first subset of decisions. The mathematical optimization algorithm is used to select a sequence in which decisions in the second optimal decision set are to be considered. The mathematical optimization algorithm takes as input the second optimal decision set, the decision template, the chaotic event information, the at least one constraint, and the user profile. The sequence is stored.

Optimal Decision Feedback Equalizer (DFE) coefficients are determined from a channel estimate by casting the DFE coefficient problem as a standard recursive least squares (RLS) problem and solving the RLS problem. In one embodiment, a fast recursive method, e.g., fast transversal filter (FTF) technique, is used to compute the Kalman gain of the RLS problem, which is then directly used to compute MIMO Feed Forward Equalizer (FFE) coefficients. The FBE coefficients are computed by convolving the FFE coefficients with the channel impulse response. Complexity of a conventional FTF algorithm may be reduced to one third of its original complexity by selecting a DFE delay to force the FTF algorithm to use a lower triangular matrix. The length of the DFE may be selected to minimize the tap energy in the FBE coefficients or to ensure that the tap energy in the FBE coefficients meets a threshold.

Methods, systems, and apparatuses are provided for controlling an environmental maintenance system that includes a plurality of sensors and a plurality of actuators. The operation levels of the actuators can be determined by optimizing a penalty function. As part of the penalty function, the sensor values can be compared to reference values. The optimized values of the operation levels can account for energy use of actuators at various operation levels and predicted differences of the sensor values relative to the reference values at various operation levels. The predicted difference can be determined using a transfer model. An accuracy of the transfer model can be determined by comparing predicted values to measured values. This accuracy can be used in determining new operational levels from an output of the transfer model (e.g., attenuating the output of the transfer model based on the accuracy).

Intelligent image parsing for anatomical landmarks and / or organs detection and / or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.

Multi-Input-Multi-Output (MIMO) Optimal Decision Feedback Equalizer (DFE) coefficients are determined from a channel estimate h by casting the MIMO DFE coefficient problem as a standard recursive least squares (RLS) problem and solving the RLS problem. In one embodiment, a fast recursive method, e.g., fast transversal filter (FTF) technique, then used to compute the Kalman gain of the RLS problem, which is then directly used to compute MIMO Feed Forward Equalizer (FFE) coefficients gopt. The complexity of a conventional FTF algorithm is reduced to one third of its original complexity by choosing the length of a MIMO Feed Back Equalizer (FBE) coefficients bopt (of the DFE) to force the FTF algorithm to use a lower triangular matrix. The MIMO FBE coefficients bop are computed by convolving the MIMO FFE coefficients gopt with the channel impulse response h. In performing this operation, a convolution matrix that characterizes the channel impulse response h extended to a bigger circulant matrix. With the extended circulant matrix structure, the convolution of the MIMO FFE coefficients gopt with the channel impulse response h may be performed easily performed in the frequency domain.

A method for displaying information related to a chaotic event. A mathematical optimization algorithm is used to select an optimal decision set for a user. The mathematical optimization algorithm takes as input a decision template, chaotic event information regarding a chaotic event, and a user profile. The optimal decision set is displayed for the user.

The invention discloses a system, a platform and a method for virtual machine scheduling decision-making. The above-mentioned virtual machine scheduling decision-making system includes: a template management module for selecting a policy template customized according to user needs; a policy management module for creating and activating a strategy by setting parameters in the policy template; a scheduling decision calculation module for Use the strategy in the active state to perform exhaustive calculations on the current resource information and cluster operation data to obtain the optimal decision. In the present invention, the virtual machine scheduling policy is developed and deployed using the policy template of the script, the development speed is fast and the deployment is flexible, and rich alternative policies can be provided to meet the needs of users at different levels, thereby improving the system performance of the virtual machine.

The invention belongs to the field of intelligent robots, particularly relates to a robot motion decision-making method, system and device introducing an emotion regulation and control mechanism, andaims to solve the problems of robot decision-making speed and learning efficiency. The method comprises the following steps: generating a predicted state value of a next moment according to a currentaction variable and a state value by utilizing an environmental perception model; updating state-based on action variables, state values, immediate rewards An action value function network; obtaininga prediction track based on an environmental perception model, calculating a local optimal solution of the prediction track, carrying out differential dynamic programming, and obtaining an optimal decision based on the model; acquiring a model-free decision based on a current state and strategy as well as minimized state-motion functions and based on the state prediction error, the reward prediction error and the average reward value, generating an emotion response signal through an emotion processing computable model, and selecting a path decision according to a threshold value of the signal.The decision-making speed is gradually increased while learning efficiency is ensured.

The invention discloses a task unloading method based on power control and resource allocation, and relates to the field of industrial Internet of Things. The method comprises the steps: establishinga cross-domain network model of an industrial field; constructing a calculation model of an equipment task; according to the model, constructing a mixed integer nonlinear programming model for communication power control, resource allocation and calculation unloading problems; decomposing a problem into three sub-problems; solving an optimal communication power and a resource allocation strategy by utilizing convex optimization knowledge, a Lagrange multiplier method and a KKT (Karush-Kuhn-Tucker) condition; after substituting an original target function, solving an optimal decision of a taskcalculation position by utilizing a deep reinforcement learning algorithm, and obtaining an optimal strategy of communication power, resource allocation and the calculation position of task unloading.The method can obtain the optimal strategy in industrial network task unloading, and has the technical effects of reducing the task delay, reducing the equipment energy consumption and ensuring the service quality.

An environment-economic robust dispatching method for power system based on classified uncertain sets is disclosed. The method constructs uncertain sets of wind power, photovoltaic power and load based on classified probability opportunity constraints. Furthermore, the robust multi-objective optimal dispatching model of power system environment economy based on classified uncertain sets is proposed, which takes robustness as the objective of collaborative optimization and considers economy and environmental protection comprehensively, so as to realize multi-objective optimal decision. The invention fully takes into account the randomness distribution characteristics differences of wind power, photovoltaic power and load, and realizes accurate description of robustness of optimized dispatching scheme. For the first time, robustness is taken as the objective of collaborative optimization, which eliminates the subjectivity of preset robustness (or confidence), and leads to more reasonablerobustness and higher overall satisfaction.

A method for displaying information related to a chaotic event. A mathematical optimization algorithm is used to select a first optimal decision set for a user. The mathematical optimization algorithm takes as input a decision template, chaotic event information, at least one constraint, and a user profile. A heuristic algorithm is used to eliminate a first subset of decisions. The first subset of decisions is in the first optimal decision set. A second optimal decision set is formed. The second optimal decision set comprises the first optimal decision set less the first subset of decisions. The mathematical optimization algorithm is used to select a sequence in which decisions in the second optimal decision set are to be considered. The mathematical optimization algorithm takes as input the second optimal decision set, the decision template, the chaotic event information, the at least one constraint, and the user profile. The sequence is stored.

The invention relates to a differential evolution random forecast classifier-based photovoltaic array fault diagnosis method. The method comprises the steps of firstly, collecting photovoltaic array voltages under various working conditions and currents of photovoltaic strings, and performing identification on various working conditions by different identifiers; secondly, determining a quantity range of decision trees in a random forest model by adopting an out-of-bag data-based classification misjudgment rate mean value; thirdly, performing global optimization on the quantity range of the decision trees by utilizing a differential evolution algorithm to obtain an optimal decision tree quantity value; fourthly, substituting the calculated optimal decision tree quantity value into a randomforecast classifier, and training samples to obtain a random forecast fault diagnosis training model; and finally, performing fault detection and classification on a photovoltaic array by utilizing the training model. According to the method, the model training speed can be greatly increased while the optimal model classification accuracy is ensured, so that the fault detection and classificationof the photovoltaic power generation array are realized more quickly and accurately.

The invention discloses a road traffic network emergency evacuation route generation method based on the Internet of vehicles. The method involves a vehicle-mounted device, a roadside device, a network background server device and a vehicle emergency evacuation route decision making model. A wireless data communication module of the vehicle-mounted device broadcasts vehicle information to the roadside device through a wireless network, the roadside device performs statistics on the vehicle information of the road section and the local road network region, and the vehicle information is transmitted to the network background server in a wired data communication mode; a multi-target vehicle emergency evacuation route optimal decision making model is established and resolved by a vehicle emergency evacuation route planning module of the network background server with the minimum evacuation total travel distance, the minimum evacuation travel time and the minimum road section congestion probability as the targets, and an optimal route is obtained to guide vehicles in a dangerous region to be evacuated to a safe region. The road traffic network emergency evacuation route generation method based on the Internet of vehicles has the advantage that a real-time, safe, reliable and efficient emergency evacuation route can be provided for vehicles in the road traffic network.

The invention relates to a moving horizon method-based multi-unmanned aerial vehicle cooperative attack task allocation method and belongs to the multi-unmanned aerial vehicle coordinated control technical field. The method includes the following steps that: the ability function of unmanned aerial vehicles is established, a calculation method of a multi-unmanned aerial vehicle cooperative attack position is provided through the jacobian matrix of the ability function; a unmanned aerial vehicle damage cost index function, a voyage cost index function and a multi-unmanned aerial vehicle cooperative task assignment model are established; and a moving horizon method is utilized to model a maneuver decision-making problem into an optimized control problem, and a whole maneuver target approach process is discretized temporally and spatially, optimal maneuver strategies can be solved piecewise, and an optimal decision-making method for a multi-unmanned aerial vehicle cooperative attack task can be provided. With the horizon method-based multi-unmanned aerial vehicle cooperative attack task allocation method of the invention adopted, a higher target gain value and damage efficiency can be obtained, and multi-unmanned aerial vehicle cooperative attack ability can be improved.

The present invention is a system, method and computer program for generating an optimal decision based on general, incomplete decision-making input, such as incomplete preferences. Input may be provided from a variety of entities (including human and computer entities). The present invention may be operable to utilize such input to make a set of decisions and an optimal decision may be efficiently generated, even if the input represents incomplete voter preferences. The present invention may also undertake a decision-making process that involves a facility to compute minimax regret and to elicit preferences from a voter. Preferences may be elicited by one or more queries posed to a voter about their pairwise preferences in such a way so as to maximally reduce minimax regret. The type of queries and order of queries posed may be determined in accordance with the most efficient decision-making process to arrive efficiently at the optimal decision. In this manner the present invention may guide the decision-making process to support and elicit efficient decision-making.

The invention discloses an unmanned combat aerial vehicle maneuvering gaming method with intuition fuzzy information. The method comprises steps of firstly, establishing a strategy set of an unmannedcombat aerial vehicle maneuvering decision; then, carrying out multi-attribute intuition fuzzy estimation on selectable maneuvering strategies according to the threatening size so as to obtain an intuition fuzzy payment matrix of unmanned combat aerial vehicle maneuvering gaming; establishing a unmanned combat aerial vehicle maneuvering gaming planning mode with intuition fuzzy information under an undetermined environment; and finally, using an improved differential evolution algorithm, solving the mode so as to obtain the optimal maneuvering gaming strategy of the unmanned aerial vehicle under the undetermined environment. According to the invention, the optimal decision problem of the unmanned combat aerial vehicle maneuvering under the undetermined environment is mainly solved.

The invention discloses an intelligent vehicle driving decision method based on a generating countermeasure network, which comprises establishing a driving decision model and driving decision control.The invention processes the driving image based on the generated antagonistic network, can process the vehicle driving path planning under the non-ideal road condition, and improves the executabilityof the end-to-end neural network. The invention extracts the most essential characteristics of driving images through the generated antagonistic network processing, maps the driving data of differentsources into a unified virtual domain, realizes the application of reinforcement learning to a real vehicle, improves the generalization of the network and adapts to the ability of different samples.For the input of the driving image, the input image used each time is the first several frames of the current time stamp video image. The predicted images obtained by this method can be used to judgethe driving decision-making planning to a greater extent. As the basis for predicting the optimal decision of the vehicle, the invention builds a bridge from the reinforcement learning to the real vehicle application.

The invention provides a method for computing a decision path, which is applied to distributed nodes. The method comprises the following steps of storing a self-generated new block into an own decision tree; periodically broadcasting own decision tree information at the current moment on a block chain network; receiving decision tree information broadcasted by other distributed nodes; after the decision tree information broadcasted by the other distributed nodes is received at each time, computing to obtain an optimal decision path according to the own decision tree information and the decision tree information broadcasted by other distributed nodes; when a set second period comes, selecting one optimal decision path as a global optimal decision path from the optimal decision path obtained by own computation and the optimal decision path obtained by computation of the other distributed nodes; and carrying out correction computation to obtain a newest method for computing the optimal decision path according to the global optimal decision path. Through adoption of the technical scheme of the invention, the correct decision path can be computed through the distributed nodes, and the computation is more accurate.

The present invention discloses a muiti-stage active distribution network self-healing planning method based on bi-level planning. The method comprises the following steps: carrying out investigation and analysis on a planned region, and determining a planning target and a decision variable; listing objective functions according to the planning target and the decision variable, wherein, an external planning objective function is a net present value, and an internal planning objective function comprises a DG reduction amount and an active load reduction amount; listing an external planning bound term and an internal planning bound term; generating an active distribution network self-healing planning model according to the internal planning objective function, the external planning objective function and the related constraint terms; and optimizing the active distribution network self-healing planning model and then carrying out calculation to obtain an optimal decision. According to the muiti-stage active distribution network self-healing planning method based on bi-level planning, which is disclosed by the present invention, long-term investment cost and short-term operating economy of an active distribution network are fully considered, thereby improving asset utilization rates of an energy storage system and a line in a grid to the greatest extent.

The invention provides a thermal power generating unit operation optimization method and device based on consumption difference analysis. The method comprises the following steps: a thermal power generating unit working process model is established based on an improved least square support vector machine algorithm; improved particle swarm optimization is adopted for optimizing parametric variables affecting the net coal consumption rate in the thermal power generating unit working process model, and a global optimum affecting the net coal consumption rate is output; operation of a thermal power generating unit is guided according to parametric variable data corresponding to the global optimum so as to make the thermal power generating unit operate at the optimal net coal consumption rate. According to the thermal power generating unit operation optimization method and device based on consumption difference analysis, the modeling method of the improved least square support vector machine algorithm is used for converting the nonlinear problem of steam working in the thermal power generating unit into the linear problem in a high-dimensional plane, an optimal decision variable is obtained finally, the optimal decision variable is used for guiding the operation of the thermal power generating unit to make the thermal power generating unit operate at the optimal net coal consumption rate, and therefore the energy utilization rate and economic benefit of the thermal power generating unit are increased.

A method is disclosed for deriving an optimum decision criteria to detect forward link power control bits by a base station from a reverse link signal. In managing the forward link received power, a mobile station commands the base station to incrementally alter the forward link transmit power, by sending periodic power control bits to the base station on a reverse link signal. The transmitted power control bits may be distorted by channel imperfections and multipath effects. The method disclosed derives an optimum decision variable for performing power control bit estimation at the base station. In a preferred embodiment, the optimum decision variable is computed by considering the in-phase and quadrature components of a single power control group received on the reverse pilot channel. Both the in-phase and quadrature components of the power control are respectively made up of a pilot part containing the reverse pilot signal which is repeated over a first fixed chip duration, and a power control part containing the forward power control bit, which is repeated over a second fixed chip duration. The optimum decision variable is obtained by first summing the respective in-phase and quadrature pilot parts over their respective first chip durations, summing the respective in-phase and quadrature power control parts over their respective in-phase and quadrature components, the respective multiplication results are then summed to yield a single result.

The present invention relates to a system and a method of making optimal medical decisions. In one embodiment presented for illustration the system comprises a quantitative model of the disease in the form of transition probabilities, the quantitative model of the effect of the medical treatment (therapy, drug or remedy) on the course of the disease, the quantitative model of costs and benefits, including monetary as well as non-monetary costs and benefits, and the model of preferences with respect to the costs and benefits. Using probabilistic inference, distributions of parameters of models are extracted from the data and the opinions of parties involved in the medical treatment. An expectation of the value of the treatment is computed. Optimality of the treatment is achieved by choosing the treatment or its parameters that give the greatest value given the evidence, the models, and the preferences. Other embodiments are discussed.

The invention belongs to the field of intelligent traffic, and provides a self-adaption traffic signal control system and method based on deep reinforcement learning. According to the self-adaption traffic signal control system and method based on deep reinforcement learning, real-time interaction of the intersection environment and a controller is achieved by using an interaction module, namely the traffic state of an intersection is collected in real time by a state sensing module, and an optimal decision scheme of the present traffic state is given through a control decision module; and meanwhile, a control core (Q value network ) in the controller can be continuously updated by adopting a framework of reinforcement learning through an update module, and thus the optimal effect of a future control scheme is improved. According to the self-adaption traffic signal control system and method based on deep reinforcement learning, various influencing factors can be synthetically collectedin both dimensions of time and space; a recurrent neural network is used for improving the extraction capability and the generalization capability of characteristics of a high-dimensional input matrix; and the requirements of complexity, instantaneity, dynamics, randomness, adaption and the like in self-adaption traffic signal control can be met, the traffic control efficiency in the intersectionis improved, and travel delaying is reduced.

The invention discloses a predictive routing method for a bus delay tolerant network (DTN). The method specifically comprises the following steps of: (1) disclosing an interval algebra-based extract network topological representation method for the semidefiniteness of a bus net node motion mode; (2) quantitatively calculating the possibility of future contact by utilizing the historical contact information of a node and adopting Bayesian estimation to obtain the probability of the future contact and a density function thereof; and (3) calculating an optimal decision sequence for a future communication path by adopting an iteration and recursion algorithm according to obtained future contact information. In a bus net scenario, a delivery rate is higher than those of most of other DTN routing, a high overhead rate and a good average delay are ensured, and the requirements of the bus DTN can be met.

The invention relates to a foreground theory-based power generator auxiliary quotation system and method for an electric power spot market. The system comprises a login information input module, a power generation enterprise data collection module, a transaction center database, a market operation information collection module, a target time period load prediction module, a spot market price prediction module, an auxiliary quotation module and an output display module. The method comprises the steps that an auxiliary quotation module builds a power generation company spot market bidding strategy and a power generation enterprise electric power spot market comprehensive decision model based on a foreground theory according to a power generation enterprise income function; and obtaining theoptimal electric quantity distribution proportion of the power generator in the plurality of markets and the optimal quotation of the spot market, and determining the optimal decision scheme. The method is reasonable in design, can be applied to system process design in actual work such as power transaction, and provides effective support for power generation enterprises to participate in power transaction in the power spot market.

The invention discloses a comprehensive evaluation method for the operating state health degree of a direct-current system for a station. The method includes the following steps that: A, an evaluationindex system taking factors such as equipment health, operation defects, service life, voltage and current operating status of the direct-current system into consideration is built; B, index values are obtained through statistical calculation, and the index values are standardized and normalized; subjective and objective weights are calculated on the basis of an improved analytic hierarchy process and standard deviation and average difference algorithms; and D, and two quantitative evaluation results of the operating state health degree of the direct-current system to be evaluated are obtained through a linear weighting method. According to the comprehensive evaluation method, an evaluation process takes into account not only the subjectivity and objectivity of evaluation, but also embodies the degree of the importance of different evaluation indexes on the evaluation results; the evaluation results tend to be more reasonable and can better distinguish the change process of the healthdegree of the operating state of the system in a more microcosmic and detailed manner; and quantitative basis can be provided for optimal decision-making in the maintenance and repair programs of thedirect-current system.