Dipankar Deb

Harsh S. Dhiman, Dipankar Deb

Globally, wind energy has lessened the burden on conventional fossil fuel based power generation. Wind resource assessment for onshore and offshore wind farms aids in accurate forecasting and analyzing nature of ramp events. From an industrial point of view, a large ramp event in a short time duration is likely to cause damage to the wind farm connected to the utility grid. In this manuscript, ramp events are predicted using hybrid machine intelligent techniques such as Support vector regression (SVR) and its variants, random forest regression and gradient boosted machines for onshore and offshore wind farm sites. Wavelet transform based signal processing technique is used to extract features from wind speed. Results reveal that SVR based prediction models gives the best forecasting performance out of all models. In addition, gradient boosted machines (GBM) predicts ramp events closer to Twin support vector regression (TSVR) model. Furthermore, the randomness in ramp power is evaluated for onshore and offshore wind farms by calculating log energy entropy of features obtained from wavelet decomposition and empirical model decomposition.

Amit Kumar Mishra, Abhishek Kumar, Dipankar Deb

In this paper, we introduce a cerebral cortex inspired architecture for robots in which we have mapped hierarchical cortical representation of human brain to logic flow and decision making process. Our work focuses on the two major features of human cognitive process, viz. the perception action cycle and its hierarchical organization, and the decision making process. To prove the effectiveness of our proposed method, we incorporated this architecture in our robot which we named as Cognitive Insect Robot inspired by Brain Architecture (CIRBA). We have extended our research to the implementation of this cognitive architecture of CIRBA in multiple robots and have analyzed the level of cognition attained by them