Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models
Aiming at the problem that the standard RRT algorithm (rapidly exploring random tree) using the pseudo-random sequence leads to uneven and unreasonable distribution of sampling points, and there are redundant sections and redundant nodes in the path of mobile robots from the starting point to the target point, a halton & dijkstra & rapidly exploring random tree(HDRRT) algorithm is proposed, which uses the Halton sequence with good uniform distribution of sampling points for sampling, and uses the candidate point set strategy to filter nodes to eliminate redundant nodes. At the same
As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimiz
An effective way to improve the performance of deep neural networks in most computer vision tasks is to improve the quantity of labeled data and the quality of labels. However, in the analysis and processing of medical images, high-quality annotation depends on the experience and professional knowledge of experts, which makes it very difficult to obtain a large number of high-quality annotations. Therefore, we propose a new semi-supervised framework for medical image classification. It combines semi-supervised classification with unsupervised deep clustering. Spreading label information to
In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed