Thangal Kunju Musaliar College of Engineering, Kollam

About: Thangal Kunju Musaliar College of Engineering, Kollam is a based out in . It is known for research contribution in the topics: Photovoltaic system & Encryption. The organization has 661 authors who have published 507 publications receiving 2442 citations.

Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach

Abstract: The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

Abstract: Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.

Computer Aided Detection of Skin Cancer

Abstract: Skin cancers are the most common form of cancers in humans It is a deadly type of cancer affecting skin Most of the skin cancers are curable at initial stages So an early detection of skin cancer can save the patients Conventional diagnosis method for skin cancer detection is Biopsy method It is done by removing or scraping off skin and that sample undergoes a series of laboratory testing It is painful and time consuming one Computer based skin cancer detection is more advantageous to patients, by which patients can identify the skin cancer without going to hospital or without the help of a doctor Computer based detection uses imaging techniques and Artificial Intelligence The different stages of detection involves- collection of dermoscopic images, filtering the images for removing hairs and noises, segmenting the images using Maximum Entropy Threshold, feature extraction using Gray Level Co-occurrence Matrix(GLCM), and classification using Artificial Neural Network(ANN) Back-Propagation Neural (BPN) Network is used for classification purpose It classifies the given data set into cancerous or non-cancerous