33 results about "Preference function" patented technology

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 primary character. A second character model comprises a second set of attribute values for the plurality of attributes of a secondary character. The primary and secondary characters are associated with first and second predetermined salience values, respectively. 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 secondary 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. Media is recommended to a user based on the media rating.

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.

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.

In an embodiment, an electronic device may include storage containing processor-executable instructions, a preference function that maps weights indicating likely user preferences for the range of values of a device setting in relation to a range of values of a variable, and a current automated device control schedule configured to control the device setting of the electronic device in relation to the variable, and a processor. The instructions may cause the processor to determine the current automated device control schedule based on the preference function by detecting user behavior that indicates satisfaction or dissatisfaction with values of the device setting in relation to the variable, updating the preference function based on the detected user behavior, and determining the current automated device control schedule by comparing a number of candidate device control schedules against the weights of the preference function and selecting the candidate with the highest score.

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 discloses a distributed base station clustering method based on the hedonic game. Base stations are clustered using the game theory. From the perspective of the game theory, the process in which base stations form a stable cluster by dynamically adjusting the cluster structure thereof is a game. In a hedonic game model, base stations are rational game players. An appropriate preference function is defined. Base stations make decisions in line with their own interests by calculating the preference value. Finally, individual equilibrium is achieved, and a stable cluster is obtained. Compared with a heuristic method, the method has a great advantage in performance. Compared with a clustering method based on the ergodic thought, the method is much less complex under the premise of little difference in performance.

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.

The invention aims to overcome the disadvantages in the prior art, and provides a natural gas purification process modeling optimization method based on an unscented kalman neural network. The natural gas purification process modeling optimization method comprises the following steps: determining input variables; collecting process production data; carrying out preprocessing on the data; carrying out data normalization processing; adopting the unscented kalman neural network to carry out modeling on the data to obtain a model; designing a preference function by using two output variables of the model of the unscented kalman neural network, and applying a multi-target genetic algorithm to optimize the input variables; disaggregating the input variables after being optimized and bringing in the model of the unscented kalman neural network in sequence, calculating the two output values of the model at the time, comparing with a sample value average value, and observing the optimization effect. The method can establish an accurate and reliable high sulfur natural gas purification desulfurization industrial process model, the yield of the finished product can be improved based on the model, the energy consumption in the desulfurization process can be reduced, and the method has important practical significance for guiding practical industrial production.

The invention discloses a method for modeling and optimizing the purification process of high-sulfur natural gas based on the detection of abnormal working conditions. Independent element analysis is used to extract independent elements, and the corresponding SPE statistics are calculated, and then compared with the set control limits to judge abnormal working conditions. The sample data collected under these conditions are discarded; the operating parameters of the purification process are used as the input variables of the extreme learning machine to establish a process model for the purification and desulfurization of high-sulfur natural gas. The output of the model is the content of H2S and CO2 in the purified gas. The particle swarm optimization algorithm optimizes the model structure of the extreme learning machine; the physical programming preference function designs different physical quantities of energy consumption and production under the same measurement criterion, and MOGA can realize the Pareto optimal solution set corresponding to the process operation parameters, energy consumption and production. The present invention utilizes the particle swarm optimized extreme learning machine to establish a statistical model for the purification and desulfurization process of high-sulfur natural gas, which improves the accuracy of the model; meanwhile, it also realizes multi-objective optimization of conflicting energy consumption and output.