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5 results about "Discrete choice" patented technology
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In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis. On the other hand, discrete choice analysis examines situations in which the potential outcomes are discrete, such that the optimum is not characterized by standard first-order conditions. Thus, instead of examining “how much” as in problems with continuous choice variables, discrete choice analysis examines “which one.” However, discrete choice analysis can also be used to examine the chosen quantity when only a few distinct quantities must be chosen from, such as the number of vehicles a household chooses to own and the number of minutes of telecommunications service a customer decides to purchase. Techniques such as logistic regression and probit regression can be used for empirical analysis of discrete choice.
Resource aggregation scheduling method and device for multi-element flexible resource virtual power plant and medium
The invention discloses a resource aggregation scheduling method and device for a multi-element flexible resource virtual power plant and a medium, and relates to the field of power system operation control, and the method comprises the steps: constructing a virtual power plant multi-target collaborative aggregation optimization model; decision variables of the optimization model comprise a discrete selection variable, a continuous power variable and a satisfaction variable; on the basis of the model, a hybrid architecture combining a deep Q network and near-end strategy optimization is adopted, and a virtual power plant aggregation decision-making agent is constructed; the mixed action space of the intelligent agent is jointly formed by a discrete selection variable and a continuous power variable; in the virtual power plant operation simulation platform, training an intelligent agent through experience playback and a target network mechanism, and constructing real-time state data of the virtual power plant into an instance of a state space; and inputting the instance of the state space into the trained agent to generate a target aggregation scheduling strategy. According to the method, joint optimization of two types of decisions is realized through an intelligent agent of a hybrid architecture, and a scheduling strategy scheme is ensured to approach global collaborative optimization.
Owner:STATE GRID SICHUAN ECONOMIC RES INST
Black box code search model backdoor attack method based on learnable discrete code transformation
PendingCN121659308APlatform integrity maintainanceNeural learning methodsGeneration processAlgorithm
A black box code search model backdoor attack method based on learnable discrete code transformation comprises the steps that a learnable backdoor generator is constructed, and malicious codes with backdoors are generated on the premise that internal parameters of a damaged model are not accessed through the learnable discrete code transformation. Firstly, an agent model capable of simulating victim model behaviors is trained through query-response data; secondly, on the proxy model, a discrete selection process is differentiable by using a re-parameterization sampling technology, and a backdoor generator is trained in combination with a multi-objective loss function so as to realize effectiveness and concealment of backdoor implantation; according to the method, the limitation of a black box is successfully bypassed by constructing the proxy model, and a new possibility is provided for an attacker. A backdoor generation process is integrated into a differentiable training framework through learnable discrete transformation, so that end-to-end learnability is realized, and an attack strategy can be automatically optimized.
Owner:NANJING UNIV OF POSTS & TELECOMM
Charging load probability spatiotemporal distribution prediction method considering random fluctuations of commuting demand
This application belongs to the field of power system management, specifically disclosing a method for predicting the spatiotemporal distribution of charging load probabilistically, taking into account the random fluctuations in commuting demand. This application constructs a multi-dimensional travel strategy set to simulate users' comprehensive decisions regarding energy, time, and space. Uncertainty is introduced from the commuting demand side, and Monte Carlo sampling is used to generate demand fluctuation samples, fundamentally characterizing the random fluctuations in load. A utility evaluation model is constructed based on cumulative prospect theory to correct for users' bounded rationality characteristics. A multi-layered nested discrete choice model is adopted, decomposing the decision into three levels: charging, departure, and route, and iterating to traffic network equilibrium using a heuristic algorithm. In the equilibrium state, the strategy selection ratio is combined with the demand sample, input into the dynamic traffic network model, and the spatiotemporal probability distribution of charging load is output. This achieves more accurate prediction of the spatiotemporal distribution of electric vehicle charging load probabilistically during evening peak hours, providing a scientific basis for charging facility planning and grid dispatching.
Owner:HUAZHONG UNIV OF SCI & TECH
Method for machine learning and computer-readable recording medium having stored therein machine learning program
A method for machine learning includes training a neural network including parameters, at least some of the parameters having a structure corresponding to an order and a coefficient of an explanatory variable of a utility function of a discrete choice model, using training data including a value of the explanatory variable and a choice result; and specifying the utility function in the neural network after being subjected to the training.
Owner:FUJITSU LTD
Graphical User Interfaces and Computing Systems for Contextual Discrete Choice Experiments
A computing system obtains multiple factors for a first environment in a first experiment. The multiple factors include one or more profile factors and one or more subject factors. Each profile factor of the one or more profile factors specifies multiple candidate features for the first environment. Each subject factor of the one or more subject factors specifies multiple candidate levels categorizing different subjects interacting with the first environment. The computing system obtains a predictive model for predicting probabilities of multiple candidate outcomes for the first environment. The predictive model has weighted model terms. The computing system receives a selection of a first level for a first subject factor of the one or more subject factors. The computing system generates a computer-generated representation of a fixed-factor model. The fixed-factor model predicts multiple candidate outcomes for a second environment with subjects defined by the selection of the first level.
Owner:JMP STATISTICAL DISCOVERY LLC