650 results about "Decision making methods" patented technology

A drive unit drives a wafer table in a Y-axis direction based on a measurement value of an encoder that measures position information of the wafer table in the Y-axis direction and based on known correction information in accordance with position information of the wafer table in a non-measurement direction (e.g. Z, θz and θx directions) that is measured by an interferometer at the time of the measurement by the encoder. That is, the wafer table (a movable body) is driven in the Y-axis direction based on the measurement value of the encoder that has been corrected by correction information for correcting a measurement error of the encoder that is caused by a relative displacement of a head and a scale in the non-measurement direction. Accordingly, the movable body can be driven with high accuracy in a desired direction while measuring the position by the encoder, without being affected by the relative motion between the head and the scale in directions other than a direction to be measured (measurement direction).

The invention discloses a ship collision avoidance assisted decision-making method and system based on track fusion and track prediction. The method comprises the following steps: S1, ship information transmitted by an automatic identification system of a ship and a radar is obtained and pretreatment is carried out on the ship information to obtain position, speed, and orientation information of the ship; S2, track association and track fusion are carried out on the position, speed, and orientation information of ship; and S3, according to the result of the track fusion, track prediction is carried out on a target ship, and ship collision risk evaluation, avoidance decision making, and target ship posture monitoring are carried out. According to the invention, reliability and stability of the ship navigation data source are improved; safety information during the ship sailing process can be monitored intelligent in real time; and thus the ship collision can be avoided. Intelligence and miniaturization of the ship-borne equipment can be realized well; and the method and system have great practical application values.

The invention relates to a heterogeneous multi-agent collaborative decision-making method based on a depth deterministic policy gradient, belonging to the collaborative decision-making field of a heterogeneous intelligent unmanned system, comprising the following steps of: firstly, defining heterogeneous multi-agent characteristic attributes and reward and punishment rules, defining multi-agent state space and action space, and constructing multi-agent motion environment for collaboratively making decision; then, establishing an actor module for decision-making action and a critic module for evaluating feedback based on the depth-deterministic strategy gradient algorithm, and training the parameters of the learning model; using the trained model to obtain the multi-agent state sequence; and evaluating the situation of the multi-agent motion state sequence according to the reward and punishment rules set in the environment. The invention may construct reasonable sports environment according to actual needs, achieve the purpose of intelligent sensing and strategy optimization through the synergy between multiple agents in the system, and has a positive effect on the development of the unmanned system field in China.

The invention discloses a multi-airport collaborative delivery system flight sorting and decision making method. The method includes three processes of 1) preprocessing of a civil aviation flight schedule, 2) building of a multi-airport collaborative delivery decision-making algorithm model, and 3) implementation of a multi-airport collaborative delivery decision-making algorithm. By the method, a unified delivery sequence can be provided to air traffic control units, airports and airlines participating in delivery so as to realize utilization maximization of airspace resources, improve air traffic control operation efficiency and guarantee air traffic safety.

Methods are disclosed for the simplification and integration of data from a diverse set of sources for environmental costs and societal benefit data. The methods allow for the computation of five basic metrics: material use, water use, energy use, toxics emitted, land use and overall pollutants emitted. Further, they facilitate the computation of complementary metrics, as well as the estimation of net present value of costs of unrealized environmental impacts.

The invention provides a reinforcement learning based air combat maneuver decision making method of a UAV. A motion model of an airplane platform is created; principle factors that influence the air combat situation are analyzed; on the basis of the motion model and analysis on the air combat situation factors, a dynamic fuzzy Q learning model of air combat maneuver decision making is designed, and essential factors and an algorithm flow of reinforcement learning are determined; a state space of air combat maneuver decision making is fuzzified and serves as state input of reinforcement learning; typical air combat motions are selected as basic motions of reinforcement learning, and the triggering intensities of fuzzy rules are summed in a weighted manner, and a continuous motion space is covered; and on the basis of an established air combat dominant function, a return value of reinforcement learning is set in a rewards and punishment values weighing-superposing method. Thus, the autonomous maneuver decision making capability of the UAV during air combat can be improved effectively, the robustness is higher, an autonomous searching optimization performance is higher, and decisionsmade by the UAV are improved continuously in continuous simulation and learning.

The invention relates to an intelligent traffic system-based intelligent vehicle lane changing decision-making method. The method comprises the following steps of firstly, parsing the received broadcast data of an intelligent traffic system by an intelligent driving system, and extracting the event information; secondly, completing the coordinate conversion of a road event by the intelligent driving system, and obtaining an event influence factor according to the event information; finally, according to the event overall influence factor and the judgment result that the current road environment allows the lane changing decision of a vehicle, allowing the lane changing path re-planning, simultaneously calculating the desired behavior of the vehicle and changing the lane of the vehicle. According to the technical scheme of the invention, according to the attribute of a road event ahead of the vehicle and the environmental information around the vehicle, the influence factor of the current vehicle driving road is calculated. Therefore, the necessity, the security and the reliability of the lane changing decision of an automatic driving vehicle are evaluated. Meanwhile, preparations are made in advance before the lane changing of the vehicle.

The invention relates to a quick chemical leakage predicating and warning emergency response decision-making method which combines diffusion model simulation with a neural network and a gas sensor system and is applied to quick warning and aid decision making of leakage of harmful gas in an industrial park. The method includes park risk factor identification, numerical simulation, data screening, neural network training and sensor system and neural network model integration, wherein the park risk factor identification is used for identifying various possible leakage accidents, the numerical simulation includes simulating all the possible accidents to obtain a range of influences of the harmful gas, the data screening includes extracting and reconstructing an effective part in a numerical simulation result according to actual sensor layout, the neural network training includes training specific neural network models by the aid of screened data so as to acquire model parameters aiming for the specific industrial park and surrounding conditions and using redundant data for parameter validation, and sensor system and neural network model integration includes combining the models with a sensor DCS (distributed control system).

The invention discloses a vehicle forced lane changing decision-making method based on a decision-making tree model. The vehicle forced lane changing decision-making method includes the following steps: firstly, reading related data during vehicle forced parallel lane changing in real time through a sensor; secondly, importing the obtained data into a vehicle forced lane changing decision-making module based on the decision-making tree model, wherein a method for building the module includes the steps of selecting training and testing data, splitting a tree, selecting attribute threshold values, pruning the tree, building the parallel lane changing decision-making tree model based on a weka platform and verifying the accuracy of the decision-making model; finally, forming a decision-making judgment result during vehicle forced lane changing through a decision-making module, and if the decision-making judgment result is that lane changing can not be carried out, giving an alarm in real time to remind a driver of the fact that lane changing can not be carried out. By means of the vehicle forced lane changing decision-making method, negative effects, caused by a complex early-warning algorithm and excessive decision-making judgment rules, on the judgment result are reduced, the accuracy and the reliability of decision-making judgment during vehicle forced lane changing are improved, and the false alarm rate is lowered.

The invention discloses a decision making method and system based on big data. The method integrates a series of decision making influence factors commonly used for all industries in advance and has the setting function of setting all the influence factors so that users in all walks of life can customize acquisition rules, decision making rules and the like required by decision making through simple setting, and for instance, the users can set industry types and data keywords to formulate the acquisition rules. Meanwhile, a universal classification model is built in advance, data to be classified can be classified by the model on the basis of auxiliary information set by the users according to service requirements of the users, so that data required by the users can be further mined conveniently, and target data are obtained. For instance, acquired medical data can be classified in terms of diseases by the model according to the auxiliary information of the diseases set by the users, and finally decisions are made according to the decision making rules set by the users. It is clear that the decision making method and system based on big data overcome the defect that an existing decision making method is not universal.

The invention brings forward a controlled terminal controlled decision-making method and apparatus based on multiple intelligent remote controllers, for simultaneously controlling one device (hereinafter simplified as a controlled terminal) through the multiple intelligent remote controllers (such as a mobile phone and various embedded remote controllers). The apparatus comprises a controlled decision-making module embedded in the controlled terminal and remote control modules on the intelligent remote controllers. The controlled decision-making module comprises a massage processing module, an intelligent remote controller discovery module, an intelligent remote controller selection module, a public application interface module and the like. The method is as follows: arranging different priority permissions for information; the controlled terminal receiving control instructions and identity information which are sent by the multiple intelligent remote controllers; and according to the selection priority permissions of the identity information, selecting a final intelligent remote controller, and executing the control construction sent by the selected intelligent remote controller. Different selection algorithms can be selected according to needs. By using the method and apparatus, the demand for application of a signal controlled terminal by multiple remote controllers can be satisfied, and conflict-free control over the controlled terminal by the intelligent remote controllers is realized.

In a predictive action determination apparatus (10), a state observation section (12) observes a state with respect to an environment (11) and obtains state data s(t). An environment prediction section (13) predicts, based on the state data s(t), a future state change in the environment. A target state determination section (15) determines, as a target state, a future state suitable for action determination with reference to a state value storage section (14). A prediction-based action determination section (16) determines an action based on a determined target state.

The invention discloses a heart rate estimation method and device for wearable heart rate monitoring equipment. The heart rate estimation method mainly includes: removing motion artifact and tracking heart rate spectrum peak, where motion artifact removing includes: utilizing a nonlinear self-adaptive filter method to capture a nonlinear relation between noise reference signals and motion artifact noise in pulse wave signals so as to effectively eliminate motion artifact interference, adopting a binary decision-making method based on classification to judge whether filtered pulse wave signals still contain a lot of noise or not, and adopting a singular spectrum analysis method to further eliminate noise interference of the pulse wave signals still containing noise; heart rate spectrum peak tracking based on frequency spectra includes: positioning heart rate spectrum peak of each time window, namely positioning the heart rate spectrum peak on the basis of a nonlinear positioning method, and positioning the heart rate spectrum peak on the basis of a classification positioning method if the nonlinear positioning method fails. The heart rate estimation method is used for heart rate estimation and is high in calculating accuracy and low in complexity, so that enforceability of the wearable monitoring equipment is guaranteed.

The invention discloses a cloud manufacturing service resource match and combination method based on performance fusion. According to a relationship between cloud manufacturing service resources and cloud manufacturing resource requirements of users, a cloud manufacturing service resource combination is divided into two stages of match and combination, a performance fusion model of the match and the combination is constructed, and combination schemes are evaluated and sequenced by means of a dynamic multi-attribute decision-making method to filter an optimal cloud service resource combination scheme. According to the cloud manufacturing service resource match and combination method based on performance fusion, the problem that current networking manufacturing resource match methods can't be directly used for achieving cloud manufacturing service match; and simultaneously, idle manufacturing cloud can be used for automatic rent-seeking on a cloud platform and is convenient, fast, safe and cheap for customers to use, a many-to-many mapping relationship of requirements and services is n:1:m, and by means of the many-to-many mapping relationship, a multi-layer and three-dimensional manufacturing service is provided for manufacturing industries.

In an intelligent object oriented agent system, a computer implemented or user assisted method of decision making in at least one situation. The method includes the step of configuring at least one tactical agent implemented by at least one tactical agent object that includes a plurality of resources corresponding to immediate certainties, near certainties, and longer-term possibilities characterizing the at least one situation. The method also includes the steps of processing the at least one situation using the at least one tactical agent, and implementing the decision making, by at least one user or independently by at least one intelligent agent responsive to the processing step. A computer readable tangible medium stores instructions for implementing the user assisted or computer implemented method of decision making, which instructions are executable by a computer. In a preferred embodiment, the situation comprises an aerial combat situation, or other situation with moving resources.

The invention relates to a stability analyzing and optimizing method suitable for layering and zoning of an ultra-high voltage electric network, and belongs to the field of safety of the ultra-high voltage electric network. The stability analyzing method comprises an improved short-circuiting current level calculating method and an ANFIS (adaptive neural fuzzy interference system)-based safety domain optimum trend analysis. The invention also provides a stability optimizing method suitable for the layering and zoning of the ultra-high voltage electric network, and the stability optimizing method comprises the following steps of establishing a reactive optimizing model based on a layering and zoning strategy, adopting an improved genetic algorithm, and the like. The stability analyzing and optimizing method has the advantages that the stability of a receiving-end electric network is analyzed and optimized by the improved short-circuiting current level calculating method, the ANFIS-based safety domain optimum trend analysis and a reactive compensation optimizing method based on the improved genetic algorithm; the safe and stable running of the electric network can be ensured, and a quantitative support and decision reference is provided for a power company when the layering and zoning planning of a receiving-end system accessed with the ultra-high voltage is made; and the method is a reliable analyzing and decision-making method, and considerable technical benefits, economic benefits and social benefits can be created.

The invention provides a multi-cycle installment decision making method and a multi-cycle installment decision making system. The multi-cycle installment decision making method comprises the steps of: acquiring user credit investigation information of a target user according to an installment request input by a user end or an external trigger, wherein the user credit investigation information comprises anti-fraud information and credit information of the user; applying for anti-fraud scanning based on the anti-fraud information and a called anti-fraud model, so as to obtain risk rating information; applying for credit scanning based on the credit information and a called credit model, so as to obtain credit rating information; and matching the risk rating information with a risk requirement and matching the credit rating information with a credit requirement in installment decision making, so as to output matched installment decisions. The multi-cycle installment decision making method and the multi-cycle installment decision making system can extract effective user information for data processing through classifying and integrating a large amount of user information, and feed back installment decision making schemes timely and accurately.

The invention belongs to the technical field of automobile advanced auxiliary driving and unmanned driving system design and relates to a parameterized learning decision control system suitable for lane changing and lane keeping behaviors and a method. The parameterized learning decision control system suitable for the lane changing and lane keeping behaviors of the invention is designed based ona parameterized decision framework; the system comprises a learning decision-making method and a track planning controller, wherein the learning decision-making method is designed under the lane changing and lane keeping scene of a vehicle on the basis of a reinforcement learning algorithm, and the track planning controller can be correspondingly parameterized under the scene so as to adapt to straight road and bend road planning. The system is suitable for a high-level automatic driving vehicle; the adaptability of the system to the different driving behavior characteristics of different drivers is effectively improved through online learning; and therefore, the system obtains better driving performance with safety guaranteed.

The invention provides an on-line analysis and an aid decision making method for the low-frequency oscillation of an electric power system. According to information which is measured by a PMU and reflects the dynamic process of a power network, such as electric generator power angle, active power, busbar voltage phase angle, circuit active power, transformer active power, and the like, the invention monitors the low-frequency oscillation event of the power network online, identifies an oscillation mode and a strongly relevant machine set, counts participation factors, recognizes the approximate area of an oscillation center, and offers aid decision making information for restraining the oscillation on time. The invention conquers a problem that the analysis of the low-frequency oscillation is difficult since a power network model and a parameter are not exact, changes a situation that the low-frequency oscillation is lack of monitoring and analyzing method as well as can not be fast reacted in the past, and changes a situation that an exact judgment is hard to be made for the lack of the aid decision making method of the low-frequency oscillation in the past. Based on the safety pre-warning system and the aid decision making system of the low-frequency oscillation, important supports can be offered to power network operators for monitoring and analyzing the low-frequency oscillation feature of the power network and treating the low-frequency oscillation event.

The present invention claims to protect a shared bicycle management and decision-making method. The method comprises: determining a delivery area, collecting parameter data, using a fuzzy comprehensive evaluation model of DEA to construct a membership matrix to determine an indicator level of the delivery area, and supplemented by multi-objective planning, determining the delivery number of bicycles in the delivery area and the delivery number of bicycles in different delivery areas; and using SVM regression and using the collected parameters to determine the function relationship of the delivery number of shared bicycles over time through the mapping of the Gaussian kernel function in the high-dimensional space, and to determine the delivery number of bicycles at different times in the same area. According to the method provided by the present invention, a quantitative decision-making management method is proposed, a FICO based personal credit evaluation system is established, bicycleuse by users is standardized, and through multi-objective planning, an optimal dispatching and repair center is set according to the different delivery points in the region, so that the operation andmaintenance costs can be reduced.

The invention belongs to the field of mobile communication technology and especially relates to a task offload and power distribution joint decision-making method in self-organizing network Ad Hoc cloud computing, and the method comprises the steps: mobile terminals are divided, a terminal having a compute-intensive task in the Ad Hoc cloud network serves as a client, and terminals having idle resources serve as agent nodes; the client is defined as a buyer, the agent nodes are defined as sellers, a buyer and seller model is utilized to analyze behaviors of the buyer and the sellers and define a utility function; on the basis of the buyer and the sellers maximizing respective profit utility, respective optimal solutions of the buyer and the sellers are obtained through analyzing; according to the current network environment, the client selects to unload computing tasks for the agent nodes, so that client power distribution scheme is determined. According to the invention, a relationship between unloading and distribution can be effectively coordinated, task unload efficiency and system resource utilization rate are improved, and balance between system time delay and energy consumption can be improved.

The invention discloses an auxiliary decision making method and device for online battle games and a storage medium. The method comprises the steps of inputting historical game data of a player into apre-established machine learning model as a training sample and training the sample, wherein the historical game data comprises historical game data of the player and historical game results; when required precision is reached, stopping sample training to obtain a final target model; obtaining current game data of the player, and inputting the current game data of the player into the target modelto obtain a first prediction result of a current game; before the game begins, displaying the first prediction result to the player corresponding to the current game. According to the auxiliary decision making method, the current game result of the player can be predicted according to the historical game data of the player, thus the player can foresee the result of the current game before the game begins so as to determine whether or not to join the game, and the game experience feeling of the player is improved.