6 results about "Self training" patented technology

The application provides an optimization method, system and equipment for a public and railway combined transport delivery network under external set internal matching, and belongs to the technical field of logistics distribution network optimization. The method is aimed at the problem that the traditional optimization algorithm in the prior art is prone to local optimization and difficult to obtain a globally satisfactory solution, and uses a deep learning assisted genetic algorithm to solve a pre-constructed public and railway combined transport network model. The algorithm performs real-time sensing on the population evolution state through a deep Q network, dynamically and intelligently selects a genetic operation strategy, and combines an experience playback mechanism to perform self-training and parameter updating, and finally outputs an optimal two-stage public and railway combined transport hub site selection and network freight flow distribution scheme. The application uses the above optimization method, system and equipment for a public and railway combined transport delivery network under external set internal matching, effectively improves the global search capability and adaptive level of the algorithm, and can obtain a public and railway combined transport network optimization scheme which is lower in cost, better in layout, and stable and reliable.

The application discloses a single picture rain removing method based on a memory-oriented Transformer, characterized by an encoder-decoder structure with a self-supervised memory module, which can well process input pictures and better extract required features, wherein the self-supervised memory module is a neural network with memory, which can record various forms of rainfall, wherein each entry in the memory corresponds to a prototype feature of a rain pattern; a self-training mechanism is added to the self-supervised memory module, enhancing the adaptability of the algorithm to natural rain pictures. Compared with the prior art, the application can remove more rain streaks with different appearances, restore clearer background scenes, and better retain the structure and details of the background. The addition of the self-training mechanism makes the algorithm more adaptive to natural rain pictures, and good results can also be achieved on natural rain pictures.

Using processes and methods described herein, a digital twin of a physical space can train itself using sensors and other information available from the building. In some embodiments, a system to be controlled comprises a controller that is connected to sensors. This controller also has a thermodynamic model of the system to be controlled. The thermodynamic model has neurons that represent a thermodynamically coherent section of a building, such as a window. The neurons represent these portions of the controlled space using parameter values and equations that model physical behavior. A machine learning process refines the thermodynamic model by modifying the parameter values of the neurons, using sensor data gathered from the system as behavior to be matched by the thermodynamic model. The thermodynamic model may be warmed up by running the model using state data as input.

The application discloses a network traffic anomaly detection method based on interactive game, and combines a reinforcement learning algorithm; in a training process, a model receives reward and punishment signals from the outside world through feedback, thereby playing a guiding role in self training, making the interactive behavior between the model guide body and the model reach a dynamic balance state, and thereby controlling the frequency of interaction and feedback. The network traffic data processing method adopts an interactive game mode, the model guide body feeds back and evaluates the analysis and decision of the model according to a model judgment standard, and then the model continuously obtains more accurate training results in the training process according to external information. In order to better make the model guide body and the model cooperate, establish a reasonable interaction strategy of the model and a reasonable feedback strategy of the model, a dynamic Bayesian game model is used to establish a credit evaluation and updating mechanism between the two, and an equilibrium state between the two models is established through game.

Using processes and methods described herein, a digital twin of a physical space can train itself using sensors and other information available from the building. In some embodiments, a system to be controlled comprises a controller that is connected to sensors. This controller also has a thermodynamic model of the system to be controlled within memory associated with the controller. The thermodynamic model has neurons that represent distinct pieces of a controlled space, such as a piece of equipment or a thermodynamically coherent section of a building, such as a window. The neurons represent these distinct pieces of the controlled space using parameter values and equations that model physical behavior of state with reference to the distinct piece of the controlled state. A machine learning process refines the thermodynamic model by modifying the parameter values of the neurons, using sensor data gathered from the system to be controlled as ground truth to be matched by behavior of the thermodynamic model. The thermodynamic model may be warmed up by running the model using state data as input.

The invention provides an optimization method, system and device for a highway-railway combined transportation delivery network under external collection and internal distribution, and belongs to the technical field of logistics distribution network optimization. According to the method, in order to solve the problems that in the prior art, a traditional optimization algorithm is prone to falling into local optimum, and a global satisfactory solution is difficult to obtain, a deep learning assisted genetic algorithm is adopted to solve a pre-constructed highway-railway combined transportation network model. According to the algorithm, the population evolution state is sensed in real time through the deep Q network, a genetic manipulation strategy is dynamically and intelligently selected, self-training and parameter updating are carried out in combination with an experience playback mechanism, and finally an optimal secondary highway-railway combined transportation hub site selection and network cargo flow allocation scheme is output. According to the optimization method, system and equipment for the highway-railway combined transportation delivery network under the external set and internal distribution, the global search capability and the adaptive level of the algorithm are effectively improved, and a highway-railway combined transportation network optimization scheme which is lower in cost, better in layout, stable and reliable can be obtained.