Internet of Things (IoT) [26] is a new networking paradigm for cyber-physical systems that allow physical objects to collect and exchange data. In the IoT, physical objects and cyber-agents can be sensed and controlled remotely across existing network infrastructure, which enables the integration between the physical world and computer-based systems and therefore extends the Internet into the real world. IoT can find numerous applications in smart housing, environmental monitoring, medical and health care systems, agriculture, transportation, etc. Because of its significant application potential, IoT has attracted a lot of attention from both academic research and industrial development.
标签： Technologies Internet Things RFID for of
上传时间： 2020-06-08
上传用户：shancjb
The large-scale deployment of the smart grid (SG) paradigm could play a strategic role in supporting the evolution of conventional electrical grids toward active, flexible and self- healing web energy networks composed of distributed and cooperative energy resources. From a conceptual point of view, the SG is the convergence of information and operational technologies applied to the electric grid, providing sustainable options to customers and improved security. Advances in research on SGs could increase the efficiency of modern electrical power systems by: (i) supporting the massive penetration of small-scale distributed and dispersed generators; (ii) facilitating the integration of pervasive synchronized metering systems; (iii) improving the interaction and cooperation between the network components; and (iv) allowing the wider deployment of self-healing and proactive control/protection paradigms.
标签： Computational Intelligence
上传时间： 2020-06-10
上传用户：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
上传用户：shancjb
