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- V. Kannan
- K. Prakalathan
- R. Karthik
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- S. Selvi
- A. Julius
- Ramachandran Vedasendiyar
- Archana Devakannan
- Sujatha Rajaraman
- Balamurugan Rangasamy
- S. S. Nagamuthu Krishnan
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Journals
- Programmable Device Circuits and Systems
- Digital Signal Processing
- Data Mining and Knowledge Engineering
- Artificial Intelligent Systems and Machine Learning
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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Saravanan, V.
- CNTFET Based SR Flip Flop
Abstract Views :282 |
PDF Views:4
Authors
V. Saravanan
1,
V. Kannan
1
Affiliations
1 Sathyabama University, Chennai, IN
1 Sathyabama University, Chennai, IN
Source
Programmable Device Circuits and Systems, Vol 3, No 8 (2011), Pagination: 423-427Abstract
Carbon Nanotube Field Effect Transistors (CNTFET) are new nano-scaled devices to replace the MOSFET in nano-scale range to provide high performance, very dense and low power circuits. Small size of the MOSFET, below a few tens of nanometres creates the low Trans-conductance, gate oxide leakage, low ON-current, Mobility degradation and increased delay. Problems observed in the MOSFET when size is reduced are avoided in CNTFET, In case of CNTFET carbon nanotube is used as channel and high-k material is used as gate dielectric. In this paper, we present the simulation results of semi-conducting Carbon Nanotube Field Effect Transistors based logic gates and SR Flip Flop. The simulation is done using HSPICE and the current conduction of CNTFET is also analysed.Keywords
Carbon Nanotube, CNTFET, NAND Gate, SR Flip Flop.
- A Web Based System for ECG Data Transfer Using ZIGBEE/IEEE Technology
Abstract Views :213 |
PDF Views:2
Authors
Affiliations
1 Dr. N.G.P. Institute of Technology, Coimbatore, IN
1 Dr. N.G.P. Institute of Technology, Coimbatore, IN
Source
Digital Signal Processing, Vol 3, No 5 (2011), Pagination: 234-238Abstract
This paper specifies the development of a remote monitoring system for ECG signals. The system provides remote monitoring of several patients wearing a portable device equipped with ZigBee/IEEE RF module connective based on wireless sensor networks. We have designed to record on-line database, server computer used to analyze ECG signals and detect serious heart anomalies in time sent alarm to authorized medical staffs or physician through telecommunication network. The main advantages of the proposed framework are (1) The ability to detect signals wirelessly within a Body Area Network (BAN) (2) Low-power and reliable data sensing through ZigBee network nodes and (3) Optimized analysis of data through an adaptive tiered architecture that maximizes the utility of processing and computational capacity at each of three stages. We are currently building a prototype of the proposed system using in-house ECG probes and ZigBee radio modules.Keywords
Zigbee-Ieee 802.15.4 Standard, Wireless ECG Monitoring System, Mysql Database and Php Language.- Model Based Important Relations Cluster Mining in Multivariate Moment
Abstract Views :195 |
PDF Views:2
Authors
V. Saravanan
1,
S. Chitra
2
Affiliations
1 Dept. of CSE, Manakula Vinayagar Institute of Technology, IN
2 Dept of IT, Manakula Vinayagar Institute of Technology, IN
1 Dept. of CSE, Manakula Vinayagar Institute of Technology, IN
2 Dept of IT, Manakula Vinayagar Institute of Technology, IN
Source
Data Mining and Knowledge Engineering, Vol 6, No 2 (2014), Pagination:Abstract
Functional magnetic resonance imaging or functional MRI (fMRI) is a efficient neuroimaging procedure using MRI tools that dealings brain movement by detecting related changes in blood flow. The goal of fMRI data investigation is to detect correlations among brain activation and a task the subject performs during the scan. It also aims to determine correlations with the specific cognitive states, such as memory and recognition, induced in the subject. In this system, we propose a novel framework for clustering the essential fMRI signals based on their interactions and also correlation which is generated in a multivariate time series. To formalize this framework we cluster only Important Interactions based on the patient's medical records with the help of Essential Clustering Algorithm. The Essential clusters (EC) are then clustered again based on their dependencies on various brain regions. These EC's are grouped under specific models. The changes detected are mined based on the type of cluster grouped under a certain model. Our method shows that certainly increases the efficiency of the system along with increases in the effectiveness with minimal resource utilization.Keywords
Clustering, Dependencies, Brain Region, Efficiency.- Automatic Brain MRI Mining Using Support Vector Machine and Decision Tree
Abstract Views :239 |
PDF Views:3
Authors
Affiliations
1 Lady Doak College, Madurai, IN
2 Dr. N.G.P Institute of Technology, Coimbatore, IN
1 Lady Doak College, Madurai, IN
2 Dr. N.G.P Institute of Technology, Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 2 (2011), Pagination: 109-116Abstract
This paper presents a texture based classification method using Support vector machine and decision tree for diagnosis of dementia. Support Vector Machine has been proved to be an effective classifier in several applications. In this work, a comparison of linear and non-linear kernels of SVM with BPN is investigated. Rules are extracted from a trained SVM which is compared with rules extracted from BPN and C5.0. OASIS dataset is utilized for training and testing of the classifiers. Wavelet based textural features from the brain MRI images are given as input feature vectors for classification. From the analysis it is found that SVM outperforms other classifiers. Rules extracted from the trained SVM improve the comprehensibility of the classifier.Keywords
SVM, BPN, Decision Tree, MRI.- Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network
Abstract Views :239 |
PDF Views:1
Authors
S. Selvi
1,
V. Saravanan
2
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, ET
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, ET
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2438-2443Abstract
Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.Keywords
Regional Convolutional Neural Network, Deep Learning, Soil Properties, Prediction.References
- V. Chang, B. Gobinathan, A. Pinagapani, S. Kannan and G. Dhiman, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 107186-107198, 2021.
- S.B.V. Sara, M. Anand and S.S. Priscila, “Design of Autonomous Production using Deep Neural Network for Complex Job”, Materials Today: Proceedings, Vol. 58, No. 3, pp. 1-12, 2021.
- A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, Vol. 56, No. 2, pp. 1-12, 2021.
- N.V. Kousik, M. Sivaram and R. Mahaveerakannan, “Improved Density-Based Learning to Cluster for User Web Log in Data Mining”, Proceedings of International Conference on Inventive Computation and Information Technologies, pp. 813-830, 2021.
- H. Azath, M. Mohanapriya and S. Rajalakshmi, “Software Effort Estimation using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network”, Journal of Intelligent Systems, Vol. 29, No. 1, pp. 251-263, 2018.
- S. Khaki and L. Wang, “Crop Yield Prediction using Deep Neural Networks”, Frontiers in Plant Science, Vol. 10, pp. 621-632, 2019.
- H. Azath and R.S.D. Wahidabanu, “Function Point: A Quality Loom for the Effort Assessment of Software Systems”, International Journal of Computer Science and Network Security, Vol. 8, No. 12, pp. 321-328, 2008.
- J.H. Jeong, J.P. Resop and N.D. Mueller, “Random Forests for Global and Regional Crop Yield Predictions”, PLoS One, Vol. 11, No. 6, pp. 1-18, 2016.
- S. Fukuda, W. Spreer, E. Yasunaga and K. Yuge, “Random Forests Modelling for The Estimation of Mango Fruit Yields under Different Irrigation Regimes”, Agricultural Water Management, Vol. 116, pp. 142-150, 2013.
- J. Liu and C.E. Goering, “A Neural Network for Setting Target Corn Yields”, Transactions of the ASAE, Vol. 44, No. 3, pp. 705-713, 2001.
- C.J. Ransom, N.R. Kitchen and J.J. Camberato, “Statistical and Machine Learning Methods Evaluated for Incorporating Soil and Weather into Corn Nitrogen Recommendations”, Computers and Electronics in Agriculture, Vol. 164, pp 1-17, 2019.
- S.T. Drummond, K.A. Sudduth, A. Joshi and S.J. Birrell, “Statistical and Neural Methods for Site-Specific Yield Prediction”, Transactions of the ASAE, Vol. 46, No. 1, pp. 1-5, 2003.
- M. Shahhosseini, R.A. Martinez-Feria and G. Hu, “Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms”, Environmental Research Letters, Vol. 14, No. 12, pp. 1-13, 2019.
- M.M. Awad, “Toward Precision in Crop Yield Estimation using Remote Sensing and Optimization Techniques”, Agriculture, Vol. 9, No. 3, pp. 54-65, 2019.
- D. Jiang, X. Yang and N. Wang, “An Artificial Neural Network Model for Estimating Crop Yields using Remotely Sensed Information”, International Journal of Remote Sensing, Vol. 25, No. 9, pp. 1723-1732, 2004.
- A.K. Prasad, L. Chai and M. Kafatos, “Crop Yield Estimation Model for Iowa using Remote Sensing and Surface Parameters”, International Journal of Applied Earth Observation and Geoinformation, Vol. 8, No. 1, pp. 26-33, 2006.
- J.R. Romero and P.F. Roncallo, “Using Classification Algorithms for Predicting Durum Wheat Yield in the Province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179, 2013.
- S. Khaki, Z. Khalilzadeh and L. Wang, “Classification of Crop Tolerance to Heat and drought-A Deep Convolutional Neural Networks Approach”, Agronomy, Vol. 9, No. 12, pp. 833-845, 2019.
- J. You, X. Li, M. Low and D. Lobell, “Deep Gaussian Process for Crop Yield Prediction based on Remote Sensing Data”, Proceedings of International Conference on Artificial Intelligence, pp. 1-6, 2017.
- N. Kim, K. Ha, K. J., Park and J. Cho, “A Comparison between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States”, International Journal of Geo-Information, Vol. 8, No. 5, pp. 240-254, 2019.
- A.X. Wang, C. Tran and N. Desai, “Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data”, Proceedings of International Conference on Computing and Sustainable Societies, pp. 1-5, 2018.
- Q. Yang, L. Shi and J. Han, “Deep Convolutional Neural Networks for Rice Grain Yield Estimation at the Ripening Stage using UAV-based Remotely Sensed Images”, Field Crops Research, Vol. 235, pp. 142-153, 2019.
- R. Tibshirani, “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, No. 1, pp. 267-288, 1996.
- I. Goodfellow, Y. Bengio and A. Courville, “Machine Learning Basics”, Deep Learning, Vol. 1, No. 7, pp. 98-164, 2016.
- N.V. Kousik, “Privacy Preservation between Privacy and Utility using ECC-based PSO Algorithm”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 567-573, 2021.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, Proceedings of International Conference on Distributed Artificial Intelligence, pp. 277-290, 2020.
- K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.
- K.M. Baalamurugan and S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, pp. 303-307, 2018.
- Effect of Hesperidin for its Anti-Proliferative Activity on Liver Cancer and Cardio Vascular Diseases
Abstract Views :220 |
PDF Views:3
Authors
A. Julius
1,
Ramachandran Vedasendiyar
2,
Archana Devakannan
3,
Sujatha Rajaraman
4,
Balamurugan Rangasamy
5,
V. Saravanan
1
Affiliations
1 Department of Biochemistry, Sree Balaji Dental College and Hospital, Bharath University, Pallikaranai, Chennai-600 100, IN
2 Department of Biochemistry, Bharath University and Lab Incharge, Public Health and Preventive Medicine, Govt of Tamilnadu, IN
3 Public Health and Preventive Medicine, IN
4 Ashok Nagar, Tamilnadu, IN
5 Shree Manakula Vinayagar Medical College and Hospital, Pondicherry, IN
6 Department of Biochemistry, Sree Balaji Dental college and Hospital, Bharath University, Pallikaranai, Chennai-600 100, IN
1 Department of Biochemistry, Sree Balaji Dental College and Hospital, Bharath University, Pallikaranai, Chennai-600 100, IN
2 Department of Biochemistry, Bharath University and Lab Incharge, Public Health and Preventive Medicine, Govt of Tamilnadu, IN
3 Public Health and Preventive Medicine, IN
4 Ashok Nagar, Tamilnadu, IN
5 Shree Manakula Vinayagar Medical College and Hospital, Pondicherry, IN
6 Department of Biochemistry, Sree Balaji Dental college and Hospital, Bharath University, Pallikaranai, Chennai-600 100, IN
Source
Research Journal of Pharmacy and Technology, Vol 10, No 1 (2017), Pagination: 307-308Abstract
Objective is to study the Plants that have served as a rich source of medicine including anti-cancer drugs. Research has shifted from the study of using extracts of whole or parts of plants to single active principles. Identification of cytotoxic agents led to the development of anticancer therapeutics with significant success but having severe side effects. Recent advances in understanding the molecular basis of cancer and development of high thorough screening methodologies have led to the discovery of novel therapies in the form of target-based drugs. Plant metabolites like flavonoids have fewer side effects and predominantly act by their capacity to scavenge free radicals, enzyme inhibition, and/or antiproliferative activity. Hesperidin is a biologically active flavonoid, is commonly found in citrus fruits and is used in Chinese medicine. It has been shown to have several useful pharmacological properties including blood lipid lowering activity.Keywords
Hesperidin, Citrus Fruits, Chemopreventive Activity, Liver Cancer, Reducing Cholesterol.- Defending Denial of Service:State Overload Attacks
Abstract Views :158 |
PDF Views:0
Authors
Affiliations
1 Thiagarajar School of Management, Madurai-625 005, IN
2 Department of Computer Applications, Dr. NGP Institute of Technology, Coimbatore, IN
1 Thiagarajar School of Management, Madurai-625 005, IN
2 Department of Computer Applications, Dr. NGP Institute of Technology, Coimbatore, IN
Source
International Journal of Advanced Networking and Applications, Vol 2, No 3 (2010), Pagination: 719-722Abstract
In a denial-of-service (DoS) attack, an attacker attempts to prevent legitimate users from accessing information or services. By targeting your computer and its network connection, or the computers and network of the sites you are trying to use, an attacker may be able to prevent you from accessing email, web sites, online accounts (banking, etc.), or other services that rely on the affected computer. Several value-added services have been proposed for deployment in the Internet. IP multicast is an example of such a service. IP multicast[2] is a stateful service in that it requires routers to maintain State for forwarding multicast data toward receivers. This characteristic makes the service and its users vulnerable to denial-of-service (DoS) attacks. One type of attack aims to saturate the available buffer space for storing state information at the routers. A successful attack can prevent end systems from properly joining multicast groups. In this paper, we present a solution to state overload attacks;.Keywords
IP Multicast, State Overload Attack.Full Text
- Identity Based Proxy-Oriented Data Uploading and Remote Data Integrity Checking with Perfect Data in Public Cloud
Abstract Views :228 |
PDF Views:4
Authors
P. Jayasree
1,
V. Saravanan
2
Affiliations
1 Department of Computer Applications, Hindusthan College of Arts and Science, IN
2 Department of IT, Hindusthan College of Arts and Science, IN
1 Department of Computer Applications, Hindusthan College of Arts and Science, IN
2 Department of IT, Hindusthan College of Arts and Science, IN
Source
Networking and Communication Engineering, Vol 10, No 1 (2018), Pagination: 14-16Abstract
In the present days many users store their significant data in the cloud. Cloud computing is the new range in wireless world. One of the major challenging issues is data integrity/security. To guard knowledge privacy, the sensitive knowledge ought to be encrypted by the information owner before outsourcing that makes the normal and economical plaintext keyword search technique useless. The point of data security which has always been noteworthy aspect of quality, cloud computing cause a new security threats. In cloud storage systems, the server that stores the client’s data is not necessarily trusted. The existing system does not pro ide security mechanisms for storing data in clouds. To overcome these issues a no el proxy-oriented data uploading and remote data integrity checking model in identity-based public key cryptography: IDPUIC (identity-based proxy-oriented data uploading and remote data integrity checking in public cloud). For achieving the efficiency of cloud storage, the proposed system pro ides flexible data segmentation with additional authorization process among the three participating parties of client, server and a third-party auditor (TPA). We propose an identity based data storage scheme, it will resist the collusion attacks.Keywords
Cloud Computing, Identity Based Cryptography, Proxy Public Key Cryptography, Remote Data Integrity Checking.References
- Z. Fu, X. Sun, Q. Liu, L. Zhou, J. Shu, “Achieving efficient cloud search services: multikeyword ranked search over encrypted cloud data supporting parallel computing,” IEICE Transactions on Communications, vol. E98-B, no. 1pp.190-200, 2015.
- Y. Ren, J. Shen, J. Wang, J. Han, S. Lee, “Mutual verifiable pro able data auditing in public cloud storage,” Journal of Internet Technology, vol. 16, no.2, pp.317-323, 2015.
- H. Guo, Z. Zhang, J. Zhang, “Proxy re-encryption with unforgeable reencryption keys”, Cryptology and Network Security, LNCS 8813, pp. 20-33, 2014.
- B.Abinaya, Mrs.S.G.Sandhya," Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity Checking in Public Cloud, International Journal of Computer Science & Engineering Technology ", ISSN:, vol. 8, No. 03, 2229-3345 2017.
- E. Kirshanova, “Proxy re-encryption from lattices”, PKC 2014, LNCS 8383, pp. 77-94, 2014
- . Xu, H. Chen, D. Zou, H. Jin, “Fine-grained and heterogeneous proxy re-encryption for secure cloud storage”, Chinese Science Bulletin, vol.59, no.32, pp. 4201-4209, 2014.
- M.Sai Sudha ,Soma Shekar , J.Deepthi," Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity Checking in Public Cloud" International Journal of Computational Science, Mathematics and Engineering, volume-4, Issue-2, 2349-8439, 2017
- Q. Wang, C.Wang, K. Ren,W. Lou, and J. Li; “Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 5, pp. 847-859, May 2011.
- F. Seb´e, J. Domingo-Ferrer, A. Mart´ınez-Ballest´e, Y. Deswarte, J. Quisquater; “Efficient Remote Data Integrity checking in Critical Information Infrastructures. IEEE Transactions on Knowledge and Data Engineering,” 20(8):1034-1038, 2008.
- https://www.computerworld.com/article/2505135/cloud-computing/when-there-s-a-third-party-in-the-cloud.html
- Penchala.Mounika & Mr. Kodam Goutham Raju " Data Integrity And Data Uploading In Cloud With Identity Based Proxy Oriented Encryption", International Journal of Research, Volume 04, 2348-6848 Issue 14, November 2017.
- Manasa P, Anishree Abhay ," Identity Based Distributed Provable Data Possession A Method Used in Multi Cloud Environment",ijsetr, volume .04, issue.04, 2319-8885, 2015.
- G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, D. Song; “Provable Data Possession at Untrusted Stores,” CCS’07, pp. 598-609, 2007.
- G. Ateniese, R. DiPietro, L. . Mancini, G. Tsudik; “Scalable and Efficient Provable Data Possession,” SecureComm 2008, article 9, 2008.
- C. C. Erway, A. Kupcu, C. Papamanthou, R. Tamassia; “Dynamic Provable Data Possession” , CCS’09, 213-222, 2009.
- Characteristics of Different Indonesian Coals Blended with a High Ash Indian Coal
Abstract Views :210 |
PDF Views:0
Authors
Affiliations
1 SRF, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
2 Joint Director, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
3 Additional Director (Retd.), Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
1 SRF, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
2 Joint Director, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
3 Additional Director (Retd.), Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
Source
Power Research, Vol 12, No 3 (2016), Pagination: 579-590Abstract
Two Indonesian coals from different mines were blended with a high ash representative Indian coal obtained from South Eastern Coalfield Limited (SECL) mines at different proportions like 10/90, 20/80, 30/70 and 50/50. These blends were characterized for the qualitative and quantitative parameters based on additive rule. The additive/non-additive behavior of these blend proportions were studied comparing the experimentally obtained value with the calculated values based on additive rule. The results obtained indicate that the quantitative parameters like proximate and ultimate parameters were found to be additive and the qualitative parameters like ash fusion temperature and Hardgrove Grindability Index (HGI) were found to have deviation from the calculated values. Among the two different blends one blend showed additive characteristics on the HGI values while the other one showed non-additive characteristics. The Ash Fusion Temperature (AFT) was found to be non-additive in both the blends.Keywords
Two Indonesian coals from different mines were blended with a high ash representative Indian coal obtained from South Eastern Coalfield Limited (SECL) mines at different proportions like 10/90, 20/80, 30/70 and 50/50. These blends were characterized for the qualitative and quantitative parameters based on additive rule. The additive/non-additive behavior of these blend proportions were studied comparing the experimentally obtained value with the calculated values based on additive rule. The results obtained indicate that the quantitative parameters like proximate and ultimate parameters were found to be additive and the qualitative parameters like ash fusion temperature and Hardgrove Grindability Index (HGI) were found to have deviation from the calculated values. Among the two different blends one blend showed additive characteristics on the- A Study on the Slagging And Fouling Propensity of Imported Coals Blended with Indian Coal
Abstract Views :229 |
PDF Views:0
Authors
Affiliations
1 Training Division, Central Power Research Institute, Bangalore-560 080, IN
2 Materials Technology Division,CentralPowerResearchInstitute,Bangalore 560 080, IN
1 Training Division, Central Power Research Institute, Bangalore-560 080, IN
2 Materials Technology Division,CentralPowerResearchInstitute,Bangalore 560 080, IN
Source
Power Research, Vol 12, No 3 (2016), Pagination: 591-602Abstract
Coal blending excercises are becoming popular in the present day scenario with many of the Indian thermal power plants. Blending of an inferior variety of indigeneous coal with that of high quality imported coal is gaining importance. With the depleting coal reserves in the country leading to deterioration of the indigeneous coal quality and less availability of the high grade coals, it has become imperative on part of the power generation plants to import high quality coals and blend them with inferior variety of indigeneous coal and burn them in the boilers for power generation.
It is challenging to ensure that the resulting coal blend will maintain the required plant output without damaging the boiler and high temperature components.
Combustion of various coalsblends in the boiler lead to a variety of complex thermochemical reactions. The inert residue of coal combustion product which is ash is composed of complex oxides of various minerals. The quantity and the characteristic of the ash is inherent to the particular type of coal combusted. The fusion of this ash in the boiler during combustion may sometime lead to slag forming and fouling problems. This slagging and fouling phenomenon is dependent on a number of factors such as the ash chemical composition, combustion temperature, combustion atmosphere, boiler operating parameters etc., Deposition of coal ash/slag and fouling impedes the heat transfer there by increasing the Flue Gas Exit Temperature (FEGT). Ash deposits due to fouling on convective pass tube banks can block flow passages. Large deposits in the upper furnace or radiant zone dislodge and fall which may cause damages to the lower furnace pressure parts. Extreme ash deposition leads to forced outages and corrosion problems. Keeping the above in view it is important to study the various parameters related with blended coal so as to ascertain the proper blend ratios, operating temperature, boiler conditions and other factors.
In this study 3 types of imported coals and an Indian coal was blended in various proportions and the blended coals were studied for their various paratmeters such as ash fusion temperatures, chemical compositions etc., and by applying certain indices, the behavior of the residual blended coalash for its slagging and fouling propensity have been reported.
Keywords
Blending of coals, slagging propensity, fouling, boiler slags, pulverized coal firing- CFD Modelling of the Blended Coal Combustion in A Typical 210MW Indian Boiler
Abstract Views :225 |
PDF Views:0
Authors
Affiliations
1 SRF, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
2 Joint Director, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
3 Additional Director (Retd.), Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
1 SRF, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
2 Joint Director, Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
3 Additional Director (Retd.), Materials Technology Division, Central Power Research Institute, Bengaluru-560080, IN
Source
Power Research, Vol 12, No 3 (2016), Pagination: 625-636Abstract
The computational fluid dynamics (CFD) assessment was carried out for the combustion of pure Indian coal and the blends of Indian/imported coals at various proportions in a typical 210 M We Indian boiler. The input fuel mass flow rate was calculated in various cases to give the same thermal input to the boiler. The various sub models used for the CFD assessment has been described in the paper. The velocity and temperature profiles of the gas phase, combustion profile of the particles, heat flux distribution to the walls of the boiler and also the thermal efficiency of the boiler were assessed in the present work. It was found that the particle and fluid dynamics play a major role in the heat flux distribution in the boiler. The Indian and imported coal blend proportion of 80/20 showed good thermal efficiency compared to the pure Indian coal and other blend proportions.Keywords
Blended Coal, CFD, Indian Boiler, Heat Flux Distribution, Pre-mixed Blending- Handoff in 5g Ultra Dense Networks Using Fixed Sphere Precoding
Abstract Views :151 |
PDF Views:1
Authors
Affiliations
1 College of Computer Science and Information Science, Srinivas University, IN
1 College of Computer Science and Information Science, Srinivas University, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 2 (2022), Pagination: 2689-2693Abstract
It is anticipated that the millimetrewave, often known as mm-wave, technology that will be used in 5G networks will greatly enhance network capacity. The mm-wave signals, on the other hand, are prone to obstructions than the ones at lower bands; this demonstrates the impact that route loss has on the network coverage. Because of the fractal nature of cellular coverage and the different path loss exponents that apply to different directions, it has been suggested that a route loss model in a multi-directional manner for 5G UDN networks. This is due to the fact that different directions have path loss exponents. In addition, the proposed loss model is applied to the 5G ultra-dense network in order to calculate the coverage probability, association probability, and handoff probability (UDN). According to the numerical findings of this research, in 5G UDN, the influence of anisotropic path loss increases the association probability with long link distance. It has also come to light that the performance of the handoff suffers tremendously as a consequence of the anisotropic propagation environment. A new difficulty has arisen for 5G UDN as a consequence of the substantial handoff overhead that has been produced.Keywords
Fractal Characteristics, Multi-Directional Path Loss, Cellular Coverage Ultra-Dense NetworkReferences
- M. Ramalingam, S. Vinothkumar and P. Ramya, “Predictive Handoff Management in Vehicular Networks Using both Weight Value Based and K-Means Algorithm Based Clustering Algorithm to Meet Desired QoS”, Journal of Physics: Conference Series, Vol. 1969, No. 1, pp. 1-12, 2021.
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- Enhanced Frost Filter and Cosine Tanimoto Classsification based Natural Disaster Management with Satellite Images
Abstract Views :141 |
PDF Views:1
Authors
M. Nirmala
1,
V. Saravanan
2
Affiliations
1 Department of Computer Applications, Hindusthan College of Engineering and Technology, IN
2 Department of Information Technology, Hindusthan College of Arts and Science, IN
1 Department of Computer Applications, Hindusthan College of Engineering and Technology, IN
2 Department of Information Technology, Hindusthan College of Arts and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2751-2758Abstract
Natural disasters are utmost incidents inside the earth's system that lead to sudden demise or bruise to humans, and destruction of precious materials, involving buildings, conveyance systems, farming land, forest and natural environment. Occurrences of economic losses due to natural disasters have resulted owing to the escalated susceptibility of the society globally and also due to weather-related disasters. Satellite image sensing remains the hypothetical instrument for disaster management as it provides information spanning wide-reaching areas and also at short time period. In this work we plan to develop a method called, Enhanced Frost Filter and Tanimoto Similarity Classification (EFF-TSC) for efficient disaster management using satellite images is proposed. The EFF-TSC method for disaster management is split into three steps. They are pre-processing, segmentation and classification. With the input image collected from satellite image database, first preprocessing is performed to preserve important features at the edges and remove the noisy pixel by means of an Enhanced Frost Filter Preprocessing model. Second, to the pre-processed satellite image, Threshold Pixel Segmentation is applied to partition into multiple segments. Finally, to the partitioned images, Tanimoto Similarity Classification is applied to classify the segmented image into two types, namely disastrous image and non-disastrous image. With this, an efficient disaster management is carried out with better accuracy and minimal time consumption. The application of the study is demonstrated using the Disaster image data set collected from Kaggle during the 2017. The results show the capability of the proposed EFF-TSC method for disaster management across time and space from different images with considerable accuracy by also reducing peak signal to noise ratio with considerable time. The findings also suggest that the potential for forensic analysis of disasters using pixel segmentation and classification based on collected images can be utilized to several locations affected by disasters.Keywords
Disaster Management, Frost Filter, Threshold Pixel Segmentation, Tanimoto Similarity Classification, Satellite Image.References
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- Chao Fan, Fangsheng Wu and Ali Mostafavi, “A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations from Social Media in Disasters”, IEEE Access, Vol. 8, pp. 10478-10490, 2020.
- Lokabhiram Dwarakanath, Amirrudin Kamsin, Rasheed Abubukar Rasheed, Anitha Anandhan and Liyana Shuib, “Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review”, IEEE Access, Vol. 10, pp. 1-13, 2021.
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- Conversion of Biomedical Wastes to Energy by Plasma Technologies
Abstract Views :128 |
PDF Views:0
Authors
Affiliations
1 Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
1 Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
Source
Power Research, Vol 17, No 2 (2021), Pagination: 75-80Abstract
The Biomedical Wastes (BMW) include variety of materials like plastics, radioactive elements, metals, infectious biomolecules, etc which are hazardous and pose potential health risk to the people when they are directly released to environment. There are many technologies for treating biomedical wastes like incineration, steam sterilisation before landfilling, etc. The plasma gasification is the state of art technology for the safe disposal of BMW and also convert them to energy. The plasma gasification operates at very high temperatures and the conversion percentage is relatively high compared to any other gasification technologies. The concept of plasma gasification for BMW and the other techno economical aspects are discussed in this paper.Keywords
Biomedical, Biomedical Waste, Plasma Gasification, Microwave, Waste to EnergyReferences
- Messerle VE, et al. Processing of biomedical wastes in plasma gasifier. Waste Management. 2018; 79(2018):791–9. https://doi.org/10.1016/j.wasman.2018.08.048 PMid:30343813
- Guidelines for Handling of Bio Medical Wastes for Utilisation, CPCB, India; 2019.
- Guidelines for handling, treatment and disposal of waste generated during treatment/diagnonsis/quarantine of COVID-19 patients, CPCB, India; 2020
- Byun Y, Cho M, Hwang S-M, Chung J. Thermal plasma gasification of municipal solid waste. Gasification for Practical Applications; 2012. p. 183–210.
- Plasma gasification commercialization [Internet]. Available from: https://en.wikipedia.org/wiki/Plasma_gasification_commercialization
- Plasma gasification. [Internet]. Available from: https://en.wikipedia.org/wiki/Plasma_gasification
- Characteristics of the HGI Fractions of the Indian Coal Blended with Imported Coals
Abstract Views :129 |
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Authors
Affiliations
1 Materials Technology Division, Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
1 Materials Technology Division, Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
Source
Power Research, Vol 17, No 2 (2021), Pagination: 121-124Abstract
The blends of a high ash Indian coal with three coals of different foreign origins -Australia, Russia and Indonesia were subjected to HGI testing as per ASTM D 409 and the resulted fractions obtained in the coarser and finer portions of ASTM 200 mesh (75 microns) were assessed for their characteristics in respect of proximate and ultimate parameters, alpha quartz and combustion reactivity. The similar studies were also carried out for the parent coals. HGI values were found additive in case of Indian-Indonesian coal blends and the same was found non-additive in respect of Indian-Russian and Indian-Australian coal blends. Considerable variation in properties was observed in the coarser and finer fractions of the ASTM 200 mesh for parent Indian and Indonesian coals compared to parent Australian and Russian coals. This indicates the disproportion of the coals and coal blends particles during the sieving process after subjecting to HGI test. The variation of alpha quartz content in the coarser and finer fractions indicate that there is a segregation of free minerals in the coarser and finer fractions. The conversion plots obtained through TGA for the coarser and finer fractions indicate that there is no maceral segregation.Keywords
Blended coal, Disproportionation, Hardgrove Grindability Index (HGI), Non-Additive, Thermogravimetric Analysis (TGA)References
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- Estimation of Fly ASH Availability in a Thermal Power Plant for Cement Manufacturers
Abstract Views :115 |
PDF Views:0
Authors
Affiliations
1 Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
1 Central Power Research Institute, Bengaluru – 560012, Karnataka, IN
Source
Power Research, Vol 17, No 2 (2021), Pagination: 125-131Abstract
The Indian coals are having 25 to 45% ash content and huge quantity of fly ash is being generated every day in the Indian thermal power plants. The fly ash so generated are being disposed by dry or wet mode from the power plant. The fly ash disposed in dry mode is lifted by the cement manufacturers and the fly ash disposed in wet mode is unattended. Most of the quantity of fly ash is collected in the ESPs. The particle size of fly ash collected in ESPs is finer compared to the fly ash collected in other parts of the power plant. For this reason the cement manufacturers prefer the fly ash from ESPs. The quantity of fly ash collected in the ESPs is not directly measurable on everyday basis as there are no well proven instrumental methods. Also the quantity of fly ash collected in ESPs fluctuates every day due to the varying load factor and coal quality. However, it is important to estimate the quantity of fly ash collected by ESPs on everyday basis so that the proportion of fly ash lifted by the cement manufacturers and the fly ash sent to the ash pond will be known. Presently power plants do not have a method to estimate the exact availability of fly ash and it is being theoretically calculated from the design value that the 80% of the total ash is fly ash and in that a fixed proportion (about 70% of total fly ash generated) is collected in the ESPs. However, the actual generation of fly ash would be different and this is influenced by the type of coal used, fineness of the input coal particles, boiler operating conditions, load factor, age of the power plant, etc. This uncertainty leads to disputes between cement manufacturers and the utility if there is a penalty clause in the agreement for not completely lifting the available fly ash (as theoretically calculated by the utility). In view of this it is imperative to formulate acceptable methods for determining the actual quantity of fly ash collected in the ESPs on daily basis. In the present work, a simple methodology was developed to quantity the average fly ash collected in ESPs in a 210 MWe coal fired power plant on every day basis through site measurements and routine power plant data. The amount of fly ash disposed in dry and wet mode has also been estimated through this method.Keywords
: Ash Disposal in Power Plants, Fly Ash, Cement Plants, Coal based Power Plants.References
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- Classification of Social Media Content and Improved Community Detection (C&CD) Using VGGNet Learning and Analytics
Abstract Views :78 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3181-3186Abstract
Social media platforms generate vast amounts of data, necessitating efficient content classification and community detection methods. This study addresses this challenge through the utilization of VGGNet analytics, a powerful deep learning architecture. We employed a two-step approach, beginning with VGGNet-based content classification to categorize social media posts. Subsequently, a community detection algorithm was applied to identify distinct user groups based on their interactions and content preferences. This research contributes an novel framework that seamlessly integrates VGGNet for content analysis and community detection, enhancing the understanding of user behavior in social media platforms. The proposed method aims to provide more accurate and insightful results compared to traditional approaches. Our experiments on diverse social media datasets demonstrate the effectiveness of the VGGNet-based approach. The content classification accurately assigns posts to relevant categories, while the community detection algorithm identifies cohesive user groups. The results highlight the potential for improved content recommendation systems and targeted marketing strategies.Keywords
Community Detection, Content Classification, Social Media, VGGNet Analytics, Deep Learning.References
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- T. Xiang and N. Goharian, “ToxCCIn: Toxic Content Classification with Interpretability”, Proceedings of International Conference on Artificial Intelligence, pp. 1-8, 2021.
- A.S. Raamkumar and H.L. Wee, “Use of Health Belief Model-Based Deep Learning Classifiers for Covid-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study”, JMIR Public Health and Surveillance, Vol. 6, No. 3, pp. 1-12, 2020.
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