30 results about "Adaptive behavior" patented technology

Methods for operating, such as methods for dispersing and clustering, robotic devices (i.e., “robots”) employ adaptive behavior relative to neighboring robots and external (e.g., environmental) conditions. Each robot is capable of receiving, processing, and acting on one or more multi-device primitive commands that describe a task the robot will perform in response to other robots and the external conditions. The commands facilitate a distributed command and control structure, relieving a central apparatus or operator from the need to monitor the progress of each robot. This virtually eliminates the corresponding constraint on the maximum number of robots that can be deployed to perform a task (e.g., data collection, mapping, searching, dispersion, and retrieval). By increasing the number of robots, the efficiency in completing the task is also increased.

A system and method for behavior modification through dynamic identification of behavior patterns, assistance in finding and implementing healthy alternatives to undesirable behavior patterns, preparation of dynamically variable user-specific programs, monitoring of current user activities, and presenting feedback and information to the user. The system and method further support incorporation of professional and other information into a user-specific package from which the user-specific program is determined, and synchronization and data integrity across the platforms of the system.

The invention integrates software and knowledge engineering with robotics technology to improve robot-to-robot and robot-to-human conversational interface and provide on-the-fly translations of situational requirements into adaptive behavior models and further down to service scenarios for a collaborative robot teams.

Methods for operating robotic devices (i.e., “robots”) that employ adaptive behavior relative to neighboring robots and external (e.g., environmental) conditions. Each robot is capable of receiving, processing, and acting on one or more multi-device primitive commands that describe a task the robot will perform in response to other robots and the external conditions. The commands facilitate a distributed command and control structure, relieving a central apparatus or operator from the need to monitor the progress of each robot. This virtually eliminates the corresponding constraint on the maximum number of robots that can be deployed to perform a task (e.g., data collection, mapping, searching). By increasing the number of robots, the efficiency in completing the task is also increased.

The present invention provides a method for selecting an adaptive action type of cloud service performance based on deep learning, including monitoring physical cluster data, virtual machine data, and service component data; combining constraint events agreed in SLA and real-time monitoring data to determine current service performance Whether optimization is needed: If the current data triggers a constraint event, the adaptive action type will be determined according to the adaptive method library, otherwise continue monitoring; cloud service performance self-optimization will be performed according to the determined adaptive action type; feedback learning, update the adaptive method library, Return to continue monitoring. The cloud environment itself has the characteristics of high scalability and dynamic reconstruction, so that the service performance of the cloud service is restricted by many factors in the actual operating environment. The best adaptive action is selected from the adaptive action set.

The system and method of the present invention process clarified emotional response data in order to guide next step adaptive behavior. Emotional responses with predetermined clarified intensities are sorted. The best quality clarified intensity or intensities are then selected as the ‘heart of the matter’, which is then shown to a user to reveal how the user is most uniquely responding to a particular subject or issue. The user is then given guidance on how to respond to the particular subject or issue thereby guiding the user's behavior in response to such subject or issue.

An adaptive behavior recognition method based on physical attributes, by collecting the acceleration data of the motion process as training samples, using the Kalman filter to filter the training samples, and calculating the physical attributes of a complete cycle of each type of action as the sample feature value, A variable-sized dynamic sliding window is used to process real-time data streams, and the average value and gravity of each feature value are selected as the basis for voting, and classification is performed by voting. Finally, through the incremental learning process, the sample feature value is dynamically updated, so that it gradually tends to the user's behavior habits, and achieves better behavior recognition purposes. The invention has good flexibility and adaptability to individual users, and has a high recognition rate.

The present invention relates to the technical field of automobile driving behavior analysis, in particular to an adaptive driving behavior analysis method and its device. Firstly, the vehicle driving state parameters such as rotational speed, acceleration and vehicle speed are collected, calculated, and the transmission ratio is obtained; the rotational speed is divided into m rotational speed intervals , divide the transmission ratio into n gear intervals, calculate the average value of the transmission ratio and acceleration of each speed interval and gear interval, and obtain the average value of the transmission ratio and the average acceleration, and obtain comparative data; collect real-time driving state parameters of the car and Compare with the comparison data to analyze whether the car is bad driving. The invention continuously collects the driving data of the car, and can fully establish the benchmark for the driver's driving behavior analysis. The more data, the more it can reflect the driver's normal driving behavior, and it can accurately compare the driver's bad driving behavior. It is the standard for judging step-shift vehicles and step-change cars, and the judgment accuracy is high.

The invention belongs to computer vision pattern recognition and video image processing methods, including: establishing a three-dimensional space-time sub-frame cube in the model of establishing video human behavior recognition, establishing human behavior feature space, clustering processing, updating labels, and establishing video human behavior recognition In the recognition of models and human behaviors, extract three-dimensional space-time sub-frame cubes from surveillance videos, extract human behavior features, determine the categories of human sub-behaviors in each video, and classify and merge videos with sub-category labels; and the current international understanding of Hollywood2 human behavior Compared with the highest recognition accuracy of the database, it has increased by 16.5%. Therefore, the invention has the ability to automatically extract more discriminative, adaptive, universal and invariant human behavior characteristics, reduces the over-fitting phenomenon and gradient diffusion problems in the neural network, and effectively improves human behavior in complex environments. The accuracy of recognition can be widely used in on-site video surveillance and video content retrieval.