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Vidhya, S.
- Fuzzy Based PI Controller for Basis Weight Process in Paper Industry
Abstract Views :174 |
PDF Views:2
Authors
S. Vidhya
1,
B. Nagaraj
2
Affiliations
1 PGP College of Engineering & Technology, Namakkal, IN
2 Tamilnadu News Print and Papers Ltd., IN
1 PGP College of Engineering & Technology, Namakkal, IN
2 Tamilnadu News Print and Papers Ltd., IN
Source
Fuzzy Systems, Vol 4, No 7 (2012), Pagination: 268-272Abstract
In paper industry, the paper machine unit plays a major role for paper making process. It is necessary to monitor the performance of the paper machine unit. In order to maintain the quality constraints like basis weight, consistency, moisture, colour etc. From these parameters the basis weight measurement is one of the most important process that determines the quality of paper. The PID controllers are widely used to control the parameters of basis weight measurement, because they are simple and robust. To maintain the stability of the system the PID controllers plays major role in the basis weight measurement. But the performance of the PID controller leads to less accuracy by using the conventional method namely Zeigler-Nichols method. In order to overcome these difficulties soft computing approaches are proposed for optimum performance of the controller. The fuzzy based PI controller is implemented to improve the steady state response of the basis weight process. The results of the fuzzy based PI controller are compared with the existing techniques used in industry and the simulation results shows that the fuzzy based PI controller performed better than the conventional methods. The real time results are included to prove fuzzy controller are best.Keywords
Basis Weight, Fuzzy Based PI Controller, PID Tuning.
- Zero Voltage and Zero Current Switching Buck-Boost DC/DC Converter Using Partial Resonant Circuit
Abstract Views :204 |
PDF Views:1
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
2 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
2 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
Source
Digital Signal Processing, Vol 3, No 2 (2011), Pagination: 53-58Abstract
This paper presents about a new soft switching Boost-Boost DC/DC converter using partial resonant circuit. The proposed circuit consists of conventional buck boost converter and partial resonant circuit. The switching devices in this proposed converter are operated by soft switching with a new partial resonant circuit. The partial resonant circuit consists of series connected switch-diode pair with a resonant capacitor, which is operated to a loss-less snubber capacitor. The switching control technique of the converters are simplified and its drive in constant switching frequency.The simulink model for a proposed converter is developed using MATLAB and the same is used for simulation studies. The results from the simulation studies showed that effective soft-switching zero voltage switching (ZVS) and zero current switching (ZCS) action using partial resonant circuit with constant frequency than the regenerative snubber circuit.
Keywords
Buck-Boost Converter, Partial Resonant Circuit, Regenerative Snubber Circuit, Soft-Switching Technique.- Implementation of Inverted Sine Carrier for Fortification in Single Phase Asymmetric Cascaded Seven Level Inverter
Abstract Views :168 |
PDF Views:1
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
2 Kumaraguru college of Technology, Coimbatore, IN
3 Government College of Technology, Coimbatore, IN
4 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
2 Kumaraguru college of Technology, Coimbatore, IN
3 Government College of Technology, Coimbatore, IN
4 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, IN
Source
Digital Signal Processing, Vol 3, No 2 (2011), Pagination: 59-63Abstract
Multilevel inverter (MLI) is a new breed of power converter that is suited for high power applications. Multilevel inverter is an effective and practical solution for increasing power and reducing harmonics in ac waveforms. This paper focuses on the implementation of inverted sine PWM technique for asymmetric cascaded multilevel inverter with unequal DC sources. This technique combines the advantage of inverted sine PWM technique and asymmetric cascaded multilevel inverter with unequal DC sources. Performance evaluation of the proposed PWM strategy and inverter topology is done using MATLAB/SIMULINK for single phase asymmetric multilevel inverter.Keywords
Asymmetric Multilevel Inverter, Multi-Carrier PWM, ISCPWM Technique, THD, Fundamental Output Voltage.- A Survey on Prediction of Brain Hemorrhage Using Various Techniques
Abstract Views :305 |
PDF Views:0
Authors
Affiliations
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 6 (2016), Pagination: 1-3Abstract
Objectives: The main objective of this work is to predict Subarachnoid haemorrhage (SAH) using machine learning techniques and analyzing the classification performance of various existing machine learning algorithms.
Methods: Diagnosing theSubarachnoid haemorrhage can be done efficiently by various machine learning techniques. Purpose of using Machine learning technique is to focus on factors that influence the prediction performance.
Findings: Subarachnoid haemorrhage is a stroke which is recognised by the occurrence of blood in subarachnoid space. Diagnosis of such potential disease becomes more important in the medical research area. Most widely used data mining methods for prediction tasks are decision rules, naïve Bayesian classifiers, support vector machines, Bayesian networks, and nearest neighbors. Some of the methods namely boosting, bagging and genetic algorithms have limited usage in the prediction.
Application/Improvements: The finding of this work shows that random forest classifier provides effective classification result than other machine learning techniques.
Keywords
Subarachnoid Haemorrhage, Machine Learning Techniques, Support Vector Machine, Naïve Bayesian Classifiers, Bayesian Networks, Genetic Algorithm.Full Text
References
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- U. Balasooriya, M. S. Perera. Intelligent brain hemorrhage diagnosis system. In IT in Medicine and Education (ITME), 2011 International Symposium. IEEE. 2011; 2, pp. 366-370.
- B. Sharma, K. Venugopalan. Automatic segmentation of brain CT scan image to identify hemorrhages. International Journal of Computer Applications - IJCA, 2012; 40(10), 1-4.
- J. Y. Choi, S. K. Kim, W. H. Lee, T. K. Yoo, D. W. Kim. A survival prediction model of rats in hemorrhagic shock using the random forest classifier. In2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. 2012; 5570-5573.
- U. Balasooriya, M. S. Perera. Intelligent brain hemorrhage diagnosis using artificial neural networks. In Business Engineering and Industrial Applications Colloquium (BEIAC), IEEE 2012; 128-133.
- B. Shahangian, H. Pourghassem. Automatic brain hemorrhage segmentation and classification in CT scan images. In Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on, IEEE, 2013 Sep; 467-471.
- M. M. Kyaw. Pre-segmentation for the computer aided diagnosis system. International Journal of Computer Science & Information Technology, 2013; 5(1), 79.
- H. S. Bhadauria, M. L. Dewal. Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. Signal, Image and Video Processing. 2014; 8(2), 357-364.
- E. S. A. El-Dahshan, H. M. Mohsen, K. Revett, A. B. M. Salem. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert systems with Applications. 2014; 41(11), 5526-5545.
- B. Shahangian, H. Pourghassem. Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybernetics and Biomedical Engineering, 2016; 36(1), 217-232.
- A Novel Machine Learning Approach for the Prediction of Subarachnoid Hemorrhage
Abstract Views :308 |
PDF Views:0
Authors
Affiliations
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
1 Dept of Computer Science, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
2 Dept of Information Technology, K.G.College of Arts and Science, Saravanampatti, Coimbatore-641035, Tamil Nadu, IN
Source
Indian Journal of Education and Information Management, Vol 5, No 3 (2016), Pagination: 1-8Abstract
Objectives: To predict outcome of patients with Subarachnoid Hemorrhage effectively by using novel ensemble classification method.
Methods: The different machine learning approaches are used to improve the outcome of patients with SAH prediction. One of such approach utilizes random forest classifier which is used for enhancing the prediction accuracy.
Findings: The outcome of patients with Subarachnoid Hemorrhage (SAH) prediction is helpful for guiding and caring patients. Such type of prediction is the most important in medical research area. Mostly SAH prediction is achieved by classification techniques such as decision rules, naive Bayesian classifiers, support vector machines, nearest neighbor classifiers and etc. However, these classifiers are not efficient for higher number of training cases.
Application/Improvements: In this paper, we propose a novel ensemble classification technique for effective classification. In which, a random forest classifier is introduced for providing efficient classification by integrating various machine learning algorithms. The algorithms used are C4.5, REPTree, and PART. The experimental results show that the best ensemble classifier and effectiveness of the random forest algorithm.
Keywords
Subarachnoid Hemorrhage, Decision Tree Classifier, Support Vector Machine, Naive Bayesian Classifier, Nearest Neighbor Classifier, Random Forest Algorithm.Full Text
References
- A. Lagares, P. A. Gomez, J. F. Alen, R. D. Lobato, J. J. Rivas, R. Alday, A. G De La Camara. A comparison of different grading scales for predicting outcome after subarachnoid haemorrhage. Acta neurochirurgica.2005; 147(1), 5-16.
- Y. C. Li, L. Liu, W. T. Chiu, W. S. Jian. Neural network modeling for surgical decisions on traumatic brain injury patients. International journal of medical informatics. 2000; 57(1), 1-9.
- B. A. Mobley, E. Schechter, W. E. Moore, P. A. McKee, J. E. Eichner. Neural network predictions of significant coronary artery stenosis in men. Artificial intelligence in medicine. 2005; 34(2), 151-161.
- M. Buscema, E. Grossi, M. Intraligi, N. Garbagna, A. Andriulli.M. Breda. An optimized experimental protocol based on neuro-evolutionary algorithms: application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment. Artificial intelligence in medicine. 2005;34(3), 279-305.
- T. P. Germanson, G. Lanzino, G. L. Kongable, J. C. Torner, N. F. Kassell. Risk classification after aneurysmal subarachnoid hemorrhage. Surgical neurology, 1998;49(2), 155-161.
- O. Takahashi, E. F. Cook, T. Nakamura, J. Saito, F. Ikawa, T. Fukui. Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: a Classification and Regression Tree analysis. Qjm. 2006;99(11), 743-750.
- U. Balasooriya, M. S. Perera. Intelligent brain hemorrhage diagnosis system. In IT in Medicine and Education (ITME), 2011 International Symposium onIEEE. 2011, December; 2, 366-370.
- B. Sharma, K. Venugopalan. Automatic segmentation of brain CT scan image to identify hemorrhages. International Journal of Computer Applications.2012; 40(10), 1-4.
- J. Y. Choi, S. K. Kim, W. H. Lee, T. K. Yoo, D. W. Kim. A survival prediction model of rats in hemorrhagic shock using the random forest classifier. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012, August, 5570-5573.
- B.Shahangian, H. Pourghassem. Automatic brain hemorrhage segmentation and classification in CT scan images. In Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on IEEE, 2013, September, 467-471.
- P. de Toledo, P. M. Rios, A. Ledezma, A. Sanchis, J. F. Alen, A. Lagares. Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques. IEEE Transactions on Information Technology in Biomedicine. 2009; 13(5), 794-801.
- Neural Network Architecture for Hybrid Network-On-Chip using Scalable Spiking for Man Machine Interface
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Department of Mathematics, KG College of Arts and Science, Coimbatore – 641035, Tamil Nadu, IN
2 Department of Information Technology, KG College of Arts and Science, Coimbatore – 641035, Tamil Nadu, IN
3 Department of Mathematics, Karaqpagam University, Coimbatore – 641035, Tamil Nadu, IN
1 Department of Mathematics, KG College of Arts and Science, Coimbatore – 641035, Tamil Nadu, IN
2 Department of Information Technology, KG College of Arts and Science, Coimbatore – 641035, Tamil Nadu, IN
3 Department of Mathematics, Karaqpagam University, Coimbatore – 641035, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 10, No 16 (2017), Pagination:Abstract
Hardware-based computer vision accelerators are going to be an important part of future mobile devices to satisfy the low power and data processing demand. In order to comprehend a high power potency and high turnout, the accelerator design will be massively parallelized and tailored to vision process that is a plus over software-based solutions and all-purpose hardware. In this research Spiking neural networks (SNNs) arrange to emulate scientific discipline within the class brain supported neurons parallel arrays that communicate through spike events. The opportunity to perform embedded neuromorphic circuits is supplied by SNNs, with low power consumption and high correspondence in comparison with the normal laptop paradigms of John von Neumann. Even so, the poor property and modularity shortage as shown in ancient neuron cell interconnect implementations supported shared bus topologies is barring climbable hardware operations of SNNs. In order to effectively apply SNN traffic patterns and neighborhood among neurons in the current design the Hybrid Network on Chip (H-NoC) design integrates a spike traffic compression technique, thus dropping traffic overhead and up turnout on the network provides world traffic hundreds to sustain turnout underneath bursting activity. The planned system reduces overhead and improves the performance through native routing of the neutron cell facilities that are the gifts within constant tile facility. This will increase the potency of the system. The scalability of the adopted H-NoC approach under completely different situations is shown by analytical results show, while synthesis and simulation analysis reveal, area of low-cost, and delay for each cluster severally. This methodology finds its application in various sector such as medical image processing and bio signal processing.Keywords
Hybrid Network, Hybrid Network on Chip, Neural Network, Scalable Spiking, Spiking Neural Network.Full Text
- Sinkhole Attack Detection in WSN using Pure MD5 Algorithm
Abstract Views :116 |
PDF Views:0
Authors
S. Vidhya
1,
T. Sasilatha
2
Affiliations
1 Faculty of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, IN
2 AMET University, Chennai – 603112, Tamil Nadu, IN
1 Faculty of Computer Science and Engineering, Sathyabama University, Chennai – 600119, Tamil Nadu, IN
2 AMET University, Chennai – 603112, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 10, No 24 (2017), Pagination:Abstract
Objective: To detect the sinkhole attack in AODV routing in wireless sensor network and provides the security. Methods: This employs an energy power consumption mechanism in AODV routing with external energy supplied through the battery. Security is achieved through pure MD5 algorithm detects sinkhole attack and prevents the network from severe attack. This approach provides better performance compared with normal AODV routing. The energy power consumption AODV routing selects the node in the network based on the node which is having more energy to transmit, as a neighbour. This implies that the network continues routing even though any one node in routing possesses less energy to transmit. Findings/Result: Simulation result obtained through Ns2 shows that the detection of sinkhole attack using MD5 security algorithm achieves 93% packet delivery rate, 80% throughput and 500 ms end to end delay in the network.Keywords
Detection, Energy, Intrusion, Sinkhole, SecurityFull Text