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.
The healthcare industry is expanding quickly along with new technologies like IoT (Internet of Things). The fundamental idea behind integrating IoT into healthcare facilities is to build it accessible by physicians from a remote location, allowing for simple patient-doctor contact as well as remote disease detection in an emergency situation. But for the IoT health system to provide exceptional patient’s care, it is vital to ensure a high level of safety. The health histories of the patients contain critical data which must only be available to authorized people, as any data which exists on
The development of smart cities demands automation in various things, including traffic engineering management. Intelligent Transportation System, usually abbreviated as ITS, is one of the supporting components of a smart city. One of the automation that can be handled in ITS is the detection of road marking violations. Road marking violations (especially in Indonesia) often occur at road intersections. Therefore, in this research, the detection and counting of the number of road marking violators at road intersections will be carried out. We propose user-defined markings to detect road markin
In this paper, we present an impressive face recognition model, which represents a robust improvement over the original MobileNetv2. Our model introduces the Receptive Field Block (RFB) to prevent any loss of facial feature information, expands the perceptual field, and implementing multi-scale feature fusion to enhance the model's feature extraction capability. Moreover, we have incorporated Coordinate Attention (CA) into the RFB to enhance recognition accuracy within the lightweight network. The proposed model is named CA_RFB_MobileNetv2. Our experimental results from eight public datasets d
Power quality disturbances are a major concern during the operation of utility grid. Integration of the renewable energy sources to the grid has further deteriorated the power quality due to use of power electronic converters and drives. Proper detection and classification of the various power quality disturbances is needed to identify the origin and to execute adequate mitigation measures. Here, an Artificial Neural Network (ANN) based classifier is used to identify different power quality disturbances and its performance is evaluated. A data-set consisting of nine different disturbances wit
The disposal and management of refinery oil sludge pose significant environmental challenges and sustainable approaches for remediation. This article reviews the prospective strategy of co-remediating refinery, oil sludge with food waste digestate, using the optimization and predictive abilities of the Artificial Neural Network (ANN) models. By combining of refinery oil sludge and food waste digestate not only effectively resolves the problem of waste management but also presents positive prospects for the remediation of oil-contaminated soils. This review summarizes current research on the us