In this research, multi-agent networked system cooperation is considered based on the leader-follower model, and application to smart irrigation has been carried out. Leader-follower model is proposed by the consideration of IoT sensors at the irrigation area. For the watering areas, we adopt leader-follower networked system to save water resource and electrical power driving water pump. Selected area or electrical pump from all irrigation area, it has the role of leader over the neighborhood. In order to provide conservative water control, highest water level is chosen as the leader. Leader-Follower modelling has been derived and verified. In order to calculate the reward by applying Leader-Follower model, Shapley value is considered and compared with the conventional result. From the simulation result, we verified good performance by using Leader-Follower model. The obtained result can be linked with Internet of Things (IoT), it makes us to manage and control water with web application as well.
In this paper, research on electroencephalogram (EEG) is carried out through principal component analysis (PCA) and support vector machine (SVM). PCA is used to collect EEG data characteristics to discriminate the behaviors by SVM methodology. The actual EEG signals are obtained from 18 experimenters who raised hands with meditation and actual movement during the experiments. The 16-channel data from the experiments form one data set. In order to get principal component of EEG signal, 16 features are considered from each channel and normalized. Simulation results demonstrate that two behaviors – i.e., raising hands and meditation – can be clearly classified using SVM, which is also visualized by a 2-dimensional principal component plot. Our research shows that specific human actions and thinking can be efficiently classified based on EEG signals using machine learning techniques like PCA and SVM. The result can apply to make action only with thinking.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.