33 results about "Preference function" patented technology

In an embodiment, an electronic device may include storage containing processor-executable instructions and a current setpoint schedule, and a processor configured to execute the instructions. The instructions may cause the processor to control an environmental control system based at least in part on the current setpoint schedule, and to determine the current setpoint schedule by detecting user behavior that indicates satisfaction with setpoints, based at least in part on the detected user behavior, determining a preference function that maps weights indicating user preferences for setpoints, determining candidate setpoint schedules, scoring the candidate setpoint schedules against the weights of the preference function to obtain first scores, where candidate setpoint schedules that best fit the weights of the preference function have the highest first scores, modifying the first scores to obtain second scores, and selecting the candidate setpoint schedule with the highest second score as a new current setpoint schedule.

Techniques for recommending media are described. A character preference function comprising a plurality of preference coefficients is accessed. A first character model comprises a first set of attribute values for the plurality of attributes of a first character. The first and second characters are associated with a first and second salience value, respectively. A second character model comprises a second set of attribute values for the plurality of attributes of a second character of the plurality of characters. A first character rating is calculated using the plurality of preference coefficients and the first set of attribute values. A second character rating of the second character is calculated using the plurality of preference coefficients with the second set of attribute values. A media rating is calculated based on the first and second salience values and the first and second character ratings. A media is recommended based on the media rating.

The invention provides a dynamic evolution modeling and energy-saving optimizing method of a high sulfur natural gas purification process; the method includes steps of collecting and forming a sample set after selecting the technical parameter influencing on desulphurization efficiency and performance index of a desulphurization unit; normalizing the sample set and forming a normalized sample set; selecting a training sample and a testing sample therefrom; structuring a neutral network model based on the training sample and confirming the initial state variable of the neutral network model; estimating the optimal state variable of the neutral network model by ST-UPFNN algorithm; updating the neutral network model according to the optimal state variable; respectively structuring a preference function of H2S concentration and CO2 concentration; optimizing respective upper and lower limits of the technical parameters of the H2S concentration and CO2 concentration by SPEA-II algorithm; introducing the optimized technical parameter to an updated neutral network model; calculating the optimized system performance of the technical parameter, comparing with a mean value of the system performance of the actual sample. By using the invention, the production efficiency of the high sulfur natural gas purification can be improved.

The invention provides an automotive chassis technical parameter robust design method based on a full life circle. The method comprises the following steps that (1) finished automobile parameters are input; (2) an optimal virtual prototype model is determined; (3) a mathematical model is built, wherein design variables include a controllable variable, an environmental noise factor and dynamics test parameters; (4) individual performance preferences are determined, and after the individual performance preferences are aggregated in a layered mode, an optimal robust solution is obtained through an overall performance preference function; (5) the design result is verified. Compared with the prior art, the automotive chassis technical parameter robust design method based on the full life circle has the advantages that the various information parameters in the development design stage, the manufacturing stage and the usage stage serve as the design variables, the optimal robust solution is obtained through the overall performance preference function formed by aggregating all the individual performance preferences, and optimal performance and robust performance of the full life cycle of an automotive chassis are ensured. In addition, an optimal chassis prototype model determined through simulation comparison serves as a design platform, and optimality of a chassis structure type is ensured.

A method for balancing competing attributes in a multi-attribute design optimization, which receives attributes and a set of input variables as inputs, and incorporates the variation of the input variables, is disclosed. The method receives transfer functions for the performance as a function of the input variables and for the customer assessment of the performance attributes. A preference function is constructed for each of a plurality of attributes to be balanced, the preference function defining a preferred outcome for a given set of inputs. The preference functions associated with each attribute are aggregated to define an aggregated preference function, thereby integrating the attributes. Optimal values are calculated for the set of input variables that optimize the aggregated preference function.

The invention discloses a user shopping behavior identification method based on big data, which is applicable to computer equipment. The method includes the following steps: basic data preparation; user behavior acquisition: acquiring browse, click, collection, forwarding and purchase behavior data of users when users open web pages through an acquisition module; user behavior quantification: classifying and summarizing the acquired behavior data, and establishing the functional relationship of users, commodity labels and the behavioral attention of a user on a certain commodity label; establishing a user preference matrix: establishing a user preference function according to the functional relationship of users, commodity labels and the behavioral attention of a user on a certain commodity label; establishing user similarity; and displaying commodities. In the invention, user behaviors are quantified, and user preference and similarity functions are established. The logic theory is simple and highly operable. The purchase scenario and user participation are considered, and the analysis result is more accurate.

The invention provides a modeling and optimizing method of a high-sulfur natural gas purification process oriented to energy saving and consumption reduction. The method comprises the steps that process parameters influencing the desulfurization efficiency and performance indexes of a desulfurization unit are selected and then are acquired to form sample sets; normalization is conducted on the sample sets to form normalized sample sets, and a training sample and a testing sample set are selected from the normalized sample sets; a neural network model is established based on the training sample, and initial state variables of the neural network model are determined; the optimal state variable of the neural network model is estimated by utilizing an ST-UPFNN algorithm; according to the optimal state variable, the neural network model is updated; preference functions of H2S concentration and CO2 concentration are established respectively; process parameters of H2S concentration and CO2 concentration are optimized by utilizing an MOGA algorithm, and the optimized process parameters are introduced into the updated neural network model, the system performance of the optimized process parameters is calculated, and average values of the system performance of actual samples are compared. The production efficiency of high-sulfur natural gas purification can be improved by utilizing the method.

The invention discloses a preference space Skyline query processing method based on a Spark environment. The method comprises a space Skyline query processing algorithm based on a preference functionand a space Skyline query processing algorithm based on preference priority. The method is scientific and reasonable;use is safe and convenient, the method comprises the following steps: through the effect of a space Skyline query processing algorithm based on a preference function; the spatial attributes and the non-spatial attributes of the data are integrated, and the data which does not meet the preference of any query point is filtered by utilizing the correlation, so that the size of a data set is reduced, the processing task amount is further reduced by utilizing the grid dominant relationship, and the query processing speed is increased; skyline query processing algorithm based on preference priority is used for clustering spatial data, keywords with high occurrence frequency in the class are used as text feature information of the whole class, and meanwhile, an extended R-tree index is established for spatial objects in the class; efficient space searching and filtering capabilities of the extened R-tree index are dominated and judged, so that Skyline query processing is accelerated.

The invention discloses a method and device for selecting a wireless network operating channel. The method comprises the following steps: acquiring a trigger signal of a channel preference function button; starting a wireless network card to work and simultaneously starting a beacon frame processing service according to the acquired trigger signal of the channel preference function button; acquiring a beacon management frame in a surrounding wireless environment through the beacon frame processing service, analyzing and processing the beacon management frame, and obtaining a preferred channel sorting list based on a preferred channel sorting rule; and selecting a wireless network from the preferred channel sorting list to construct the wireless network operating channel. According to the method and device disclosed by the invention, by implementing the steps of acquiring the trigger signal of the channel preference function button, starting the wireless network card to work, simultaneously starting the beacon frame processing service, receiving the beacon management frames of all channels, analyzing and processing the beacon management frames, providing the preferred channel sorting list, and selecting the wireless network to construct the wireless network operating channel, the problem of channel interference during network operation can be avoided.

The invention relates to a functional dependency-based diversity data restoration method. The method comprises the following steps of: initializing a restoration set; judging whether a restoration number in the restoration set is smaller than or equal to a set restoration number, if the judging result is positive, initializing an input queue and executing the next step, and otherwise, executing the last step; selecting a restoration element of each restoration by utilizing a preference function w'(C) so as to generate the input queue; carrying out data restoration by utilizing a Genrepair algorithm; judging whether the restoration set comprises a restoration same as the restoration or not, if the judging result is positive, directly returning the second step, and otherwise, adding the restoration into the restoration set and returning to the second step; and judging whether a termination condition is satisfied or not, if the judging result is positive, completing the restoration, and otherwise, checking the restoration set and selecting corresponding restorations to carry out replacement. Compared with the prior art, the method has the advantages of carrying out data restoration through considering both the diversity and cost, improving the restoration efficiency and being suitable for the effective dynamic sampling of restoration spaces in index levels.

A display apparatus and method are provided. The display apparatus includes a display configured to display a user interface (UI) screen, a storage configured to divide and store information about a user preference content and a user preference function according to a user preference type, and a controller configured to, in response to a predetermined event, control the display to display a UI including a graphic user interface (GUI) item indicating the user preference content and a GUI item indicating the user preference function, respectively, based on the information stored in the storage.

The invention provides a dynamic evolutionary modeling and energy-saving optimization method of the oil extraction process of an oil field machine. The method comprises the following steps of: determining an efficiency influence factor and a performance variable in an oil extraction process of the oil field machine; carrying out dimension reduction processing on a load variable in a sample to construct a new sample, and carrying out normalization on the new sample; on the basis of the normalized new sample, constructing a neural network model; utilizing a ST-UKFNN (Strong Trace Unscented Kalman Filtering Neural Network) algorithm to estimate the optimal state of a state variable formed by a weight threshold value in the neural network model; utilizing an optimal state variable to reconstruct the updated neural network model to obtain an oil extraction process model of the oil field machine; constructing a preference function of a practical daily liquid yield, and utilizing a MOGA (Multi-Objective Genetic Algorithm) to carry out optimization on the respective upper limit and lower limit of a decision variable; and introducing the optimized decision variable into the oil extraction process model of the oil field machine, calculating the system performance of the optimized decision variable, and comparing the calculated value with the average value of the system performance of a practical sample. When the method is utilized, the production efficiency of the oil field machine can be improved, and energy consumption can be lowered.

The invention relates to a voice call preferential realization method during short message sending through a service channel. The method comprises the following steps: when a voice initial call request is detected, a mobile station judges the current state; in the case of an idle state or a paging response sub-state, the mobile station sends the voice initial call request to a BSS (base station subsystem); the BSS returns a voice initial call request acknowledgement and sends a voice initial call request to an MSC (mobile switching center); the MSC judges whether to send a short message paging request; if so, the MSC suspends the short message processing process, caches the short message to be sent, and then performs standard voice initial call flow processing, and sends the cached short message in an established voice service channel; and if not, the MSC performs standard voice initial call flow processing. The method provided by the invention realizes voice preference function through processing ways on different equipment based on the judgment on different message sending states and stages when the voice request is generated, ensures preferential continuing of voice service with higher real-time requirement, and processes the two kinds of services (short message and voice) according to priority.

The invention discloses a financial risk quantification method and device based on risk preferences and a storage medium. On the basis of obtaining risk data, an index risk function and risk preference value are introduced, the risk preference is reflected in the form of the function and the variable, and the risk preference function is substituted into the preset risk quantification function, sothat the parameters are adjusted in real time according to different risk preferences of investors, and the risk quantification result more conforming to the actual situation of the investors is obtained.

The invention discloses a reliability analysis method for a hydraulic structure. The reliability analysis method comprises the steps of determining distribution of factors affecting the reliability of the hydraulic structure by utilizing stable distribution, then, generating random numbers of the factors, which correspond to a stable distribution model, combining means of the factors, which correspond to experimental data, reducing values of the factors in performance functions, and finally, calculating the failure probability of the hydraulic structure through the preference functions. The reliability analysis method is characterized in that the stable distribution and the random numbers are directly described, information of asymmetry, trailing and the like of the factors affecting the reliability of the hydraulic structure is utilized, values of test times are determined through convergence curves of the failure probability changing along with the test times, so that calculation results of the failure probability are accurate, effects of time scale effects of principal factors on the failure probability are studied, and reference information is provided for formulating reinforcement and maintenance schemes of hydraulic structures.

The invention provides an oil field mechanical extraction process decision parameter optimization method based on preference multi-objective optimization. The oil field mechanical extraction process decision parameter optimization method based on preference multi-objective optimization includes the steps: determining the efficiency influence factor and the performance variable during the oil extraction process of an oil field machine; performing dimension reduction processing on the load variable in a sample, constructing a new sample, and normalizing the new sample; based on the normalized new sample, constructing a neural network model; utilizing an ST-UPFNN algorithm to estimate the optimal state of the state variable formed by the weight thresholds in the neural network model; utilizing the optimal state variable to reconstruct the updated neural network model to obtain an oil extraction process model for an oil field machine; constructing a preference function for the practical liquid production capacity; utilizing a multi-objective evolutionary algorithm to optimize the top and bottom limitation of each decision parameter; and plugging the optimized decision variable into the oil extraction process model for an oil field machine, calculating the average value of the system performance of the optimized decision variable, and comparing with the average value of the system performance of the practical sample. The oil field mechanical extraction process decision parameter optimization method based on preference multi-objective optimization can improve the production efficiency for oil extraction of an oil extraction machine, and can reduce energy consumption.

The invention discloses an evaluation method of a reconfigurable manufacturing system, based on PROMETHEE. The evaluation method comprises the steps that a decision maker formulates evaluation schemes before making a decision; each evaluation scheme comprises a plurality of evaluation indexes; different preference functions are respectively selected according to characteristics of the evaluation indexes; each scheme is evaluated through each evaluation index and a corresponding preference index value is computed; preference indexes are computed; the total preference relation between a scheme a and other schemes in a selectable scheme set A is computed; scheme ordering rules are formulated; the schemes are comprehensively ordered according to the defined ordering rules, and a complete ordering index phi (a) is defined, so as to cover the shortage of part of ordering indexes, and an ordering result is obtained according to the complete order index phi (a). The evaluation method has the benefits that the decision process is more close to the practicality, so that a more accurate evaluation result is obtained.