Artificial Intelligence

The robot arm system with no or low-accuracy Lagrange dynamic identification is a typical unknown-structure MIMO coupled system. It is difficult to achieve fast-convergence and high-accuracy control for this practical system, especially with no empirical pre-adjustment of the initial input direction. To solve this practical problem, a novel prescribed finite-time nondirectional AT-S fuzzy control method is proposed.

The operator controls the vehicle to drive in an environment with dense distribution of obstacles. During the driving process, the spatial environment data is collected by liDAR and camera, and the map of the operable area is processed according to the change of longitudinal slope, which is used to show the distribution of the operable area in the current driving space of the vehicle. Then, according to the distribution of operable areas and driving behavior data, the study of humanoid driving is carried out.

This dataset offers a comprehensive collection of financial data associated with the development of medical software, providing insights into the various cost components involved in creating and maintaining such systems. It encompasses expenses from the initial concept and design phase through to development, testing, deployment, and ongoing maintenance. The data has been meticulously gathered from a variety of completed and ongoing medical software projects, highlighting both typical and outlier cost scenarios.

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models.