Min-A Jeong

A mesh topology formation scheme for IEEE 802.15.4 based wireless sensor networks

IEEE 802.15.4 is designed for low-cost and low-power wireless personal area networks. The network formation of the IEEE 802.15.4 does not impose constraints on the topology and it contains two topologies: the star topology, the peer-to-peer, and the tree topology. The mesh topology enables robust multi-hop communication and more flexible network, but it induces an additional complexity for providing an end-to-end connectivity between all devices in the network. In this paper, we propose the mesh topology formation scheme for IEEE 802.15.4 network.

Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed

In this paper, we propose a wind forecasting method that reflects wind characteristics to improve the accuracy of wind power prediction. The proposed method consists of extracting wind characteristics and predicting power generation. The part that extracts the characteristics of the wind uses correlation analysis of power generation amount, wind direction and wind speed. Based on the correlation between the wind direction and the wind speed, the feature vector is extracted by clustering using the K-means method. In the prediction part, machine learning is performed using the SVR that generalizes the SVM so that an arbitrary real value can be predicted. Machine learning was compared with the proposed method which reflects the characteristics of wind and the conventional method which does not reflect wind characteristics. To verify the accuracy and feasibility of the proposed method, we used the data collected from three different locations of Jeju Island wind farm. Experimental results show that the error of the proposed method is better than that of general wind power

Red Tide Algae Recognition using PCA and Roundness

Red tide is a natural phenomenon that change sea color by harmful algal blooms. There have been many studies on red tide due to increasing of red tide damage. However, to automatically classify the red tide algae is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies are used a few type of red tide algae for classification. In this paper, we proposed the red tide algae recognition method using PCA and roundness of image objects.