Nirmal Kumar A.,Information Institute of Technology
Applied Solar Energy (English translation of Geliotekhnika) | Year: 2012
Enormous potential of solar energy as a clean and pollution free source enrich the global power generation. India, being a tropical country, has high solar radiation and it lies to the north of equator between 8°4′ & 37°6′ North latitude and 68°7′, and 97°5′ East longitude. In southindia, Tamilnadu is located in the extreme south east with an average temperature of gerater than 27.5° (> 81.5 F). In this study, an adaptive neuro-fuzzy inference system (ANFIS) based modelling approach to predict the monthly global solar radia- tion(MGSR) in Tamilnadu is presented using the real meteorological solar radiation data from the 31 districts of Tamilnadu with different latitude and longitude. The purpose of the study is to compare the accuracy of ANFIS and other soft computing models as found in literature to assess the solar radiation. The performance of the proposed model was tested and compared with other earth region in a case study. The statistical performance parameters such as root mean square error (RMSE), mean bias error (MBE), and coefficient of deter mination (R2) are presented and compared to validate the performance. The comparative test results prove the ANFIS based prediction are better than other models and furthermore proves its prediction capability for any geographical area with changing meterological conditions. © Allerton Press, Inc., 2012.
Jameer Basha A.,Sri Krishna College of Engineering And Technology |
Palanisamy V.,Information Institute of Technology |
Purusothaman T.,Government College of Technology, Coimbatore
European Journal of Scientific Research | Year: 2012
Multimodal biometrics systems are becoming increasingly efficient over the unimodal system, especially for the securing mobile devices like PDA, PC tablets and, etc. In this paper we propose a novel tri-modal biometric recognition technique using teeth, fingerprint and voice as biometric traits. The matching scores of the individual traits are classified using support vector machine. The experiments were conducted over a database collected from 20 individuals with multiple instances of all the three traits. The performance analysis of the fusion techniques revealed that the equal error rates of 1.44%, 1.88% and 3.06% for the support vector machine, weighted summation and K-NN Classifier respectively. On the other hand, the equal error rates for unimodal systems are 7.4%, 8.3% and 4.6% for teeth, voice and fingerprint biometrics traits respectively. Hence, we confirmed that the proposed support vector machine fusion method outperformed other fusion techniques and unimodal classifiers. © EuroJournals Publishing, Inc. 2012.