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Anbazhagan, S.
- Criteria and Techniques of Detecting Site Specific Mechanisms for Artificial Recharge - A Case Study from Ayyar Basin, India
Abstract Views :173 |
PDF Views:2
Authors
Affiliations
1 Centre for Remote Sensing, School of Earth Sciences, Bharathidasan University, Tiruchirappalli - 620 023, IN
1 Centre for Remote Sensing, School of Earth Sciences, Bharathidasan University, Tiruchirappalli - 620 023, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 50, No 4 (1997), Pagination: 449-456Abstract
The present paper narrates how various types of geological. structural, geomorphological and subsurface geological data have been collected and integrated in various pennutations and combinations to select suitable mechanisms for artificial recharge in Ayyar basin of Tamil Nadu.Keywords
Remote Sensing, Artificial recharge, Groundwater, Tamil Nadu.- Recent Developments and Opportunities in Exploration Geology and Geoinformatics
Abstract Views :190 |
PDF Views:118
Authors
Affiliations
1 Department of Geology, Periyar University, Salem-636011, IN
1 Department of Geology, Periyar University, Salem-636011, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 70, No 1 (2007), Pagination: 173-174Abstract
No Abstract.- Evaluation of Areas for Artificial Groundwater Recharge in Ayyar Basin, Tamil Nadu, India through Statistical Terrain Analysis
Abstract Views :172 |
PDF Views:2
Authors
Affiliations
1 Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai - 400 076, IN
2 Centre for Remote Sensing, Bharathidasan University, Tiruchirappalli-620 023, IN
1 Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai - 400 076, IN
2 Centre for Remote Sensing, Bharathidasan University, Tiruchirappalli-620 023, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 67, No 1 (2006), Pagination: 59-68Abstract
The paper presents an approach of selecting suitable areas for artificial groundwater recharge structures through remote sensing and integrated terrain analysis by statistical method. The study area "Ayyar basin" is an aquifer system of gneisses and charnockite litho-Units in Tiruchirappalli district, Tamil Nadu. The area has witnessed a steady decline in groundwater table. Hence, the scope for artificial groundwater recharge was studied in this area with the help of Tamil Nadu State Council for Science and Technology (TNSCS&T). Numerical database was generated for water level, lineament density, slope, drainage density, soil type, thickness of soil, thickness of weathered zone, thickness of fractured zone and depth to bedrock through remote sensing and hydrogeological study. These nine variables were used as input parameters and factor analysis with varimax rotation was carried out. In the factor analysis, the factor in which the variables loaded significantly were considered for further analysis. Using factor scores, the domains where the water level was deeper with decrease of slope and drainage density, and increase of lineament density, thickness of soil, thickness of weathered zone, thickness of fractured zone and depth to bedrock were buffered out. The buffered domains derived from each factor were finally integrated and thus the entire study area was fragmented into a number of domains suitable for artificial recharge. Such statistical output has not only helped to identify the suitable locations for artificial recharge but also indicated their controlling terrain characteristics, thus leading to the prioritization of area for specific method of cost effective recharge.Keywords
Artificial Recharge, Remote Sensing, Lineaments, Statistical Modeling, Ayyar Basin, Tamil Nadu.- Developments of Fractures and Land Subsidence at Kolli Hills, Tamil Nadu
Abstract Views :175 |
PDF Views:2
Authors
Affiliations
1 Department of Geology, Periyar University, Salem-636011, IN
1 Department of Geology, Periyar University, Salem-636011, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 72, No 3 (2008), Pagination: 348-352Abstract
Kolli hills is one of the small tourist spots in Tamil Nadu falls in Eastern Ghats. In general, improper land management, intensive weathering and rainfall trigger frequent slope failures and landslide in hill systems. During December 2005, similar slope failure had occuued in the Southeastern part of Kolli hills. Fractures and open cracks had developed along with horizontal and vertical displacements. Continuous heavy rainfall deforestation agricultural practices and obstruction of natural flow are observed to be the causative factors for such failure. Field investigations and other parameters were studied in detail. Abandoning of current agriculture practices and permitting natural flow of the streams are immediate steps recommended for stabilising such vulnerable slope.Keywords
Fractures, Land Subsidence Landslide, Kolli Hills, Tamil Nadu.- Athens Seasonal Variation of Ground Resistance Prediction Using Neural Networks
Abstract Views :209 |
PDF Views:2
Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
1 Department of Electrical Engineering, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 1 (2015), Pagination: 1113-1116Abstract
The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN) to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN) to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.Keywords
Ground Resistance, Generalized Regression Neural Network, Forecasting.References
- F.E. Asimakopoulou, E.A. Kourni, V.T. Kontargyri, G.J. Tsekouras and I.A. Stathopulos, “Artificial Neural Network Methodology for the Estimation of Ground Resistance”, Proceedings of WSEAS International Conference on Systems, pp. 453-458, 2011.
- F.E. Asimakopoulou, G.J. Tsekouras, I.F. Gonos and I.A. Stathopulos, “Artificial Neural Network Approach on the Seasonal Variation of Soil Resistance”, Proceedings of 7th Asia-Pacific International Conference on Lightning, pp. 794-799, 2011.
- F.E. Asimakopoulou, G.J. Tsekouras, I.F. Gonos and I.A. Stathopulos, “Estimation of Seasonal Variation of Ground Resistance using Artificial Neural Networks”, Electric Power Systems Research, Vol. 94, pp. 113-121, 2013.
- Robert L. Cascio, “Safe Measurement of Ground Resistance”, Proceedings of Society for Mining, Metallurgy and Exploration Symposium, 1992.
- T. Takahashi and T. Kawase, “Calculation of Earth Resistance for a Deep Driven Rod in a Multi-layer Earth Structure”, IEEE Transactions on Power Delivery, Vol. 6, No. 2, pp. 608-614, 1991.
- C.J. Blattner, “Prediction of Soil Resistivity and Ground Rod Resistance for Deep Ground Electrode,” IEEE Transactions on Power Apparatus and Systems, Vol. PAS-99, No. 5, pp. 1758-1763, 1980.
- Y.L. Chow and M.M.A. Salama, “A Simplified Method for Calculating the Substation Grounding Grid Resistance,” IEEE Transactions on Power Delivery, Vol. 9 No. 2, pp. 736-742, 1994.
- F. Dawalibi and N. Barbetio, “Measurements and Computations of the Performance of Grounding System Buried in Multilayer Soils”, IEEE Transactions on Power Delivery, Vol. 6 No. 4, pp. 1483-1490, 1991.
- M.A. Salam, A.A. Maqrashi, N. Mohammed, Z. Nadir and M. Shahidullah, “Statistical Approach to Find an Empirical Relationship between the Grounding Resistance and Length of Buried Electrode in the Soil”, WSEAS Transactions on Circuits and Systems, Vol. 3, No. 6, pp. 1483-1486, 2004.
- M.A. Salam, S.M. Al-Alawi and A.A. Maqrashi, “An Artificial Neural Networks Approach to Model and Predict the Relationship between the Grounding Resistance and Length of Buried Electrode in the Soil”, Journal of Electrostatics, Vol. 64, No. 5, pp. 338-342, 2006.
- D.F. Specht, “A General Regression Neural Network”, IEEE Transactions on Neural Networks, Vol. 2, No. 6, pp. 568-576, 1991.
- S. Anbazhagan and N. Kumarappan, “Day-ahead Deregulated Electricity Market Price Forecasting using Neural Network Input Featured by DCT”, Energy Conversion and Management, Vol. 78, pp. 711-719, 2014.
- S. Anbazhagan and N. Kumarappan, “Day-ahead Deregulated Electricity Market Price Forecasting using Recurrent Neural Network”, IEEE Systems Journal, Vol. 7, No. 4, pp. 866-872, 2013.
- S. Anbazhagan and N. Kumarappan, “Day-ahead Price Forecasting in Asia’s First Liberalized Electricity Market using Artificial Neural Networks”, International Journal of Computational Intelligence Systems, Vol. 4, No. 4, pp. 476-485, 2011.
- MATLAB Help, “General Regression Neural Network”. Available at: http://in.mathworks.com/help/nnet/ug/generalized-regression-neural-networks.html. Accessed 05 October 2015.
- Weathering and Landslide Occurrences in Parts of Western Ghats, Kerala
Abstract Views :222 |
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Authors
Affiliations
1 Geological Survey of India, Kerala Unit Manikanteswaram P O, Trivandrum - 695 013, IN
2 Department of Geology, Periyar University, Salem - 636 011, IN
3 Department of Geology, University College, Trivandrum - 695 034, IN
4 Central Groundwater Board, Trivandrum - 695 004, IN
1 Geological Survey of India, Kerala Unit Manikanteswaram P O, Trivandrum - 695 013, IN
2 Department of Geology, Periyar University, Salem - 636 011, IN
3 Department of Geology, University College, Trivandrum - 695 034, IN
4 Central Groundwater Board, Trivandrum - 695 004, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 78, No 3 (2011), Pagination: 249-257Abstract
The climatic condition of Western Ghats has influenced the process of weathering and landslides in this mountainous tract along the southwest coast of India. During the monsoon period, landslides are a common in the Western Ghats, and its intensity depends upon the thickness of the loose unconsolidated soil formed by the process of weathering. Debris landslides with a combination of saprock, saprolite and soil, indicate the role of weathering in landslide occurrences. This paper reports on how the weathering in the windward slope of Western Ghats influences the occurrence of landslides and the factors which accelerate the weathering process. Rock and soil samples were collected from the weathering profile of hornblende gniess and granite gneiss. The chemical analysis and the calculated Chemical Index of Alteration (CIA) indicate the significant weathering and its possible influence on landslide occurrences in the study area. Mainly, the CIA value of lateritic soil and forest loam indicated the extent of high chemical weathering in this region. Rainfall is the dominant parameter influencing the chemical weathering process. In addition, deforestation, land use practices and soil erosion are some of the other important factors accelerating the weathering process and landslide occurrences in the region. The locations of the previous landslides superimposed on geology and soil show that most of the landslide occurrences are associated with the highly weathered zone, particularly lateritic soil and the 'severe' (rock outcrop) erodability zone.Keywords
Weathering, Landslides, CIA, Western Ghats, Kerala.References
- ANBAZHAGAN, S., SAJINKUMAR, S.K. and SINGH, T.N. (2011) Remote sensing and geotechnical Studies for slope failure assessment in part of Ernakulam and Idukki District, Kerala, India. In: T.N. Singh and Y.C. Sharma (Eds.), Slope Stability (Natural and Man Made Slope). Vayu Education of India, New Delhi, pp.255-281.
- BUMA, J. and DEHN, M. (1998) A method for predicting the impact of climate change on slope stability. Environ. Geol., v.35(2-3), pp.190-196.
- DEEPTHY, R. and BALAKRISHNAN, S. (2005) Climatic control on clay mineral formation: Evidence from weathering profiles developed on either side of the Western Ghats. Jour. Earth System Sci., v.114, pp.545-556.
- DEROSE, R.C., TRUSTRUM, N.A. and BLASCHKE, P.M. (2006) Post-deforestation soil loss from steepland hillslopes in Taranaki, New Zealand. Earth Surface Process and Landform, v.11(2), pp 131-144.
- DOKUZ, A. and TANYOLU, E. (2004) Geochemical Constraints on the Provenance, Mineral Sorting and Subaerial Weathering of Lower Jurassic and Upper Cretaceous Clastic Rocks of the Eastern Pontides, Yusufeli (Artvin), NE Turkey. Turkish Jour. Earth Sci., v.15, pp.181-209.
- FLUENTE, J.D.L., ELDER, D. and MILLER, A. (2002) Does deforestation influence the activity of deep-seated landslides? Observations from the flood of 1997 in the Central Klamath Mountains, Northern California. The evolving PacificNorthwest Landscape: Geomorphic and Ecologic controls, constrains, and conundrums in the Quaternary. May 13-15, 98th Annual meeting Abstracts.
- JENNY, H. (1941) Factors in soil formation. McGraw Hill, New York, 271p.
- JHA, C.S., DUTT, C.B.S. and BAWA, K.S. (2000) Deforestation and land use changes in Western Ghats, India. Curr. Sci., v.79(2), pp.231-237.
- KOSHIMOTO, S., TSUNOGAE, T. and SANTOSH, M. (2004) Sapphire and Corundum bearing ultra-high temperature rock from the Palghat-Cauvery shear system, Southern India. Jour. Mineral. Petrol. Sci., v.99, pp.298-310.
- LINDSAY, P., CAMPBELL, R.N., FERGUSSON, D.A., GILLARD, G.R. and MOORE, T.A. (2001) Slope stability probability classification, Waikato Coal Measures, New Zealand. Internat. Jour. Coal Geol., v. 45, nos.2-3, pp.127-145.
- NESBITT and YOUNG (1984) Prediction of some weathering trends of plutonic and volcanic rocks based on thermodynamic and kinetic consideration. Goechim. Cosmochim. Acta, v.54, pp.1523-1534.
- OLDHAM, R.D. (1894) The evolution of Indian Geography. The Geographical Jour., v.III(3), pp.169-179.
- PASCOE, E.H. (2001) Physical Geography of the Western Ghats. In: Y. Gunnell and B.P. Radhakrishna, (Eds.), Sahyadri: The great escarpment of the Indian subcontinent. Mem. Geol. Soc. India, no.47(1), pp.67-69.
- PASUTO, A. and SILVANO, S. (1998) Rainfall as a trigger of shallow mass movements. A case study in the dolomites, Italy. Environ. Geol., v.35(2-3), pp.184-189.
- PRICE, J.R. and VELBEL, M.A. (2003) Chemical weathering indices applied to weathering profiles developed on heterogeneous felsic metamorphic parent rocks. Chemical Geol., v. 202, pp.397-416.
- RAJAN, P.K., SANTOSH, M. and RAMACHANDRAN, K.K. (1984) Geochemistry and petrogenetic evolution of the diatexites of Central Kerala, India. Proc. Indian Acad. Sci. (Earth Planet. Sci.), v.93(1), pp.57-69.
- RANGER, J. (2002) Le cycle biogeochimique des elements nutritifs dans les ecosystems forest. INRA, Nancy, 168p.
- RASTOGI, B.K., CHANDA, R.K., KUMAR, N., SATYMURTHY, C., SARMA, C.S.P. and RAJU, I.P. (1989) Report on the Idukki earthquake of magnitude 4.5 on June 7 1988 as an example of reactivation of a NW-SE wrench fault in Peninsular India. NGRI Tech. report. No. ENVIRON 61,78.
- SAJINKUMAR, K.S. (2005) Geoinformatics in landslide risk assessment and management in parts of Western Ghats, Central Kerala, South India. PhD Thesis (Unpublished). Indian Institute of Technology Bombay, 197p.
- SANTOSH, M., IYER, S.S. and VASCONCELLOS, M.B.A. (1987) Rare earth element geochemistry of the Munnar carbonatite, Central Kerala. Jour. Geol. Soc. India, v.29, pp.335-343.
- SMYTH, C.G. and ROYLE, S.A. (2000) Urban landslide hazards: incidence and causative factors in Niteroi, Rio de Janerio State, Brazil. Applied Geography, v.20, pp.95-117.
- SOREGHAN, M.J., SOREGHAN, G.S. and HAMILTON, M.J. (2002) Geochemistry and Detrital Zircon Geochronology of Upper Paleozoic Loessite within the Ancestral Rocky Mountains: Implications for Paleoclimate. AAPG Hedberg Conference "Late Paleozoic Tectonics and Hydrocarbon Systems of Western North America, The Greater Ancestral Rocky Mountains" July 21-26 Colorado.
- SUBRAMANYA, K.R. (2001) Origin and evolution of the Western Ghats and the west coast of India. In: Y. Gunnell and B.P. Radhakrishna (Eds.), Sahyadri: The great escarpment of the Indian subcontinent. Mem. Geol. Soc. India, no.47(1), pp.463-473.
- TERLIEN, M.T.J. (1998) The determination of statistical and deterministic hydrological landslide-triggering thresholds. Environ. Geol., v.35(2-3), pp.124-130.
- TREFTHEN, J.M. (1950) Classification of sediments. Amer. Jour. Sci., v.248, pp.55-62.
- VAN ASCH, TH.W.J., DE BRUM FERREIRA, A. and RODRIGUES, M.L. (1999) The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, v30, pp.133-146.
- WILSON, R.C. and WIECZOREK, G.F. (1995) Rainfall thresholds for the initiation of debris flows at La Honda, California. Environ. Engg. Geoscience, v.1, pp.11-27.
- ZHOU, C.H., LEE, C.F., LI, J. and XU, Z.W. (2002) On the spatial relationship between landslides and causative factors on Latau Island, Hong Kong. Geomorphology, v.43, pp.197-207.
- Binary Classification of Day-Ahead Deregulated Electricity Market Prices Using Neural Network Input Featured by DCT
Abstract Views :150 |
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Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
1 Department of Electrical Engineering, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 2, No 4 (2012), Pagination: 384-390Abstract
There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could be realized using discrete cosine transforms (DCT) based neural network (NN) approach (DCT-NN). These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The binary classification NN and the proposed DCT-NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of mainland Spain.Keywords
Price Forecasting, Discrete Cosine Transforms, Neural Network, Binary Electricity Price Classification, Electricity Market.- Method Development and Validation for Simultaneous Estimation of Metamizole Sodium and Pitofenone HCl by a Stability Indicating RP-HPLC
Abstract Views :238 |
PDF Views:0
Authors
Affiliations
1 Department of Pharmaceutical Analysis, Gautham College of Pharmacy (Affiliated to Rajiv Gandhi University of Health Sciences), R.T. Nagar post, Bangalore, Karnataka-560032, IN
2 Karuna College of Pharmacy (Affiliated to Kerala University of Health Sciences), Irringuttur, Thirumittacode P.O. Palakad Dist. Kerala-679533, IN
1 Department of Pharmaceutical Analysis, Gautham College of Pharmacy (Affiliated to Rajiv Gandhi University of Health Sciences), R.T. Nagar post, Bangalore, Karnataka-560032, IN
2 Karuna College of Pharmacy (Affiliated to Kerala University of Health Sciences), Irringuttur, Thirumittacode P.O. Palakad Dist. Kerala-679533, IN
Source
Asian Journal of Research in Chemistry, Vol 4, No 9 (2011), Pagination: 1371-1377Abstract
A simple, selective, rapid and precise RP-HPLC has been developed for the simultaneous estimation of Metamizole sodium and Pitofenone HCl in presence of Fenpiverinium bromide of combined Pharmaceutical dosage forms. The Inertsil ODS 3V C-18 Column was used for Metamizole sodium and Pitofenone HCL separation. The Mobile phase used was sodium dihydrogen phosphate buffer : Methanol,(0.05M, pH 5.0), (53:47 v/v) at a flow rate of 1ml/min at 286nm. The calibration curves was linear at a concentration range of 700.90 μg/ml to 1301.73 μg/ml and 7.01 μg/ml to 13.01 μg/ml with its regression coefficient (r2=0.9992 and 0.9995) for Metamizole sodium and Pitofenone HCl respectively was obtained. The LOD and LOQ was found to be in the range of 21.6174 μg/ml and 65.507 μg/ml for Metamizole sodium and 0.1701 μg/ml and 0.5155 μg/ml for Pitofenone HCl respectively. The method is highly sensitive and successfully applied for determination of Metamizole sodium and Fenpiverinium bromide in tablet dosage forms.Keywords
Metamizole Sodium, Pitofenone HCl, Reverse Phase High Performance Liquid Chromatography.- An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices
Abstract Views :184 |
PDF Views:3
Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
2 Anubavam Technologies Private Limited, US
1 Department of Electrical Engineering, Annamalai University, IN
2 Anubavam Technologies Private Limited, US
Source
ICTACT Journal on Soft Computing, Vol 8, No 1 (2017), Pagination: 1567-1567Abstract
Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.Keywords
Electricity Price Classification, Extreme Learning Machines (ELM), Power System Market, Price Forecasting.References
- S.N. Singh, “Electric Power Generation, Transmission and Distribution”, 2nd Edition, Prentice-Hall, 2008.
- A.J. Conejo, M.A. Plazas, R. Espinola and A. B. Molina, “Day-Ahead Electricity Price Forecasting using the Wavelet Transform and ARIMA Models”, IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 1035-1042, 2005.
- H.Y. Yamin, S.M. Shahidehpour and Z. Li, “Adaptive Short-Term Electricity Price Forecasting using Artificial Neural Networks in the Restructured Power Markets”, International Journal of Electrical Power and Energy Systems, Vol. 26, No. 8, pp. 571-581, 2004.
- P. Mandal, T. Senjyu and T. Funabashi, “Neural Networks Approach to Forecast Several Hour ahead Electricity Prices and Loads in Deregulated Market”, Energy Conversion and Management, Vol. 47, No. 15-16, pp. 2128-2142, 2006.
- J.P.S. Catalao, S.J.P.S. Mariano and V.M.F. Mendes, “Short-Term Electricity Prices Forecasting in a Competitive Market: A Neural Network Approach”, Electric Power Systems Research, Vol. 77, Vo. 10, pp. 1297-1304, 2007.
- R. Gareta, L.M. Romeo and A. Gil, “Forecasting of Electricity Prices with Neural Networks”, Energy
- Conversion and Management, Vol. 47, No. 13-14, pp. 17701778, 2006.
- H.T. Pao, “Forecasting Electricity Market Pricing using Artificial Neural Networks”, Energy Conversion and Management, Vol. 48, No. 3, pp. 907-912, 2007.
- N. Amjady, “Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network”, IEEE
- Transactions on Power Systems, Vol. 21, No. 2, pp. 887896, 2006.
- N. Amjady and H. Hemmati, “Day-Ahead Price Forecasting of Electricity Markets by a Hybrid Intelligent System”, International Transactions on Electrical Energy Systems, Vol. 19, No. 1, pp. 89-102, 2009.
- N.M. Pindoriya, S.N. Singh and S.K. Singh, “An Adaptive Wavelet Neural Network-based Energy Price Forecasting in Electricity Markets”, IEEE Transactions on Power Systems, Vol. 23, No. 3, pp. 1423-1432, 2008.
- J.P.S. Catalao, H.M.I. Pousinho and V.M.F. Mendes, “Neural Networks and Wavelet Transform for Short-Term Electricity Prices Forecasting”, Proceedings of 15th International Conference on Intelligent System Applications to Power Systems, pp. 1-5, 2009.
- J.P.S. Catalao, H.M.I. Pousinho and V.M.F. Mendes, “Short-Term Electricity Prices Forecasting in a Competitive Market by a Hybrid Intelligent Approach”, Energy Conversion and Management, Vol. 52, No. 2, pp. 10611065, 2011.
- M. Shafie-khah, M.P. Moghaddam and M.K. Sheikh-ElEslami, “Price Forecasting of Day-Ahead Electricity Markets using a Hybrid Forecast Method”, Energy
- Conversion and Management, Vol. 52, No. 5, pp. 21652169, 2011.
- N. Amjady and F. Keynia, “Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm”, IEEE Transactions on Power Systems, Vol. 24, No. 1, pp. 306318, 2009.
- J. P.S. Catalao, H.M.I. Pousinho and V.M.F. Mendes, “Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting”, IEEE Transactions on Power Systems, Vol. 26, No. 1, pp. 137-144, 2011.
- H. Zareipour, A. Janjani, H. Leung, A. Motamedi and A. Schellenberg, “Classification of Future Electricity Market Prices”, IEEE Transactions on Power Systems, Vol. 26, No. 1, pp. 165-173, 2011.
- N. Amjady and F. Keynia, “Application of a New Hybrid Neuro-Evolutionary System for Day-Ahead Price Forecasting of Electricity Markets”, Applied Soft Computing, Vol. 10, No. 3, pp. 784-792, 2010.
- Spanish Electricity Market, Available at: www.omel.com, Accessed on 2017.
- O. Er, N. Yumusak and F. Temurtas, “Chest Diseases Diagnosis using Artificial Neural Networks”, Expert Systems with Applications, Vol. 37, No. 12, pp. 7648-7655, 2010.
- Extreme Learning Machines (ELM) and its Applications, Available at: http://www3.ntu.edu.sg/home/egbhuang/, Accessed on 2017.
- S. Anbazhagan and N. Kumarappan, “A Neural Network Approach to Day-Ahead Deregulated Electricity Market Prices Classification”, Electric Power Systems Research, Vol. 86, pp. 140-150, 2012.
- S. Anbazhagan and N. Kumarappan, “Day-Ahead Deregulated Electricity Market Price Classification using Neural Network Input Featured by DCT”, International Journal of Electrical Power and Energy Systems, Vol. 37, No. 1, pp. 103-109, 2012.
- S. Anbazhagan and N. Kumarappan, “Binary Classification of Day-Ahead Deregulated Electricity Market Prices using Neural Network Input Featured by DCT”, ICTACT Journal on Soft Computing, Vol. 2, No. 4, pp. 384-390, 2012.
- H. Zareipour, A. Janjani, H. Leung, A. Motamedi and A. Schellenberg, “Electricity Price Thresholding and Classification”, Proceedings of IEEE Power and Energy Society General Meeting, pp. 1-7, 2011.
- W.K. Wong and Z.X. Guo, “A Hybrid Intelligent Model for Medium-Term Sales Forecasting in Fashion Retail Supply Chains using Extreme Learning Machine and Harmony Search Algorithm”, International Journal of Production Economics, Vol. 128, No. 2, pp. 614-624, 2010.
- River Profile Modeling and Fluvial Geomorphological Evaluation of Thoppaiyar Sub-Basin Using Geoinformatics Technology
Abstract Views :196 |
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Authors
Affiliations
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN
Source
International Journal of Earth Sciences and Engineering, Vol 10, No 3 (2017), Pagination: 484-494Abstract
The curvature of river profiles has long been taken to be a fundamental indicator of the underlying processes governing fluvial erosion and thereby of landscape evolution. Longitudinal profile is a graph of distance verses elevation is an x-y plot showing bed elevation as a function of downstream distance. Due to the plate movement is considerably slow, the human history record is too short to register landscape change for such a long time scale. In the present study, an attempt has been made the quantitative analysis of geomorphic indices coupled with some mathematical models for the Thoppaiyar sub-basin and its 16 micro-basins, including the gradient index (SL), normalized gradient index (SL/k), Profile complexity index (PCI) and slope-area relationship (Slr). Based on quantitative results of these geomorphology indices, this study suggests that the important factor influencing landscape of the Thoppaiyar sub-basin. Topographic map, IRS P6 LISS III satellite data, 10 m contour interval, SRTM data and ArcGIS 9.3 software were utilized. The contour lines of topographic maps of the main river and 14 micro basins are digitized as control points. Models of the longitudinal profiles using simple mathematical functions were made considering four functions for describing the form of longitudinal profiles. The abnormally high SL and SL/k values indicated that a decreasing trend from lower to mid-stream areas and the result of slope–area relationship also indicated that the regression line of the upper and lower steam exhibit an obvious right-shift could be explained by geodynamic models of active deformation in Thoppaiyar sub-basin.Keywords
River Profile, Gradient Index, Mid-Stream, Fluvial Geomorphology, Thoppaiyar.- Drought Hazard Assessment in Ponnaiyar River Basin, India Using Remote Sensing and Geographic Information System
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Authors
Affiliations
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN
Source
International Journal of Earth Sciences and Engineering, Vol 10, No 2 (2017), Pagination: 247-256Abstract
Due to the increase of water demand and threatening climate change, in the recent years have witnessed much focus on global drought scenarios. In India, Tamil Nadu have deficient rainfall (921 mm) compared to national average of (1200 mm) leading to over-reliance on irrigation for agriculture, and increasing extraction of groundwater reserves (estimates show over 60 percent of reserves are already exhausted) pose a big issue for the future and its indicate the continuing susceptibility of the society to drought. This study demonstrates the cumulative drought hazard assessment using climatic, biophysical and social factors in the Ponnaiyar River basin, Tamil Nadu, India. It was hypothesized that the key climatic, biophysical and social factors that define meteorological drought hazard, it is rainfall, normalized deviation of rainfall whereas for agricultural drought hazard were soils, geomorphology, drainage density, land use, and relief, and for hydrological drought hazard, it is lithology, depth to water table, and surface water bodies. The construction for the derivation of an agricultural, meteorological, and hydrological drought hazard map was created through the development of a numerical weighting scheme to evaluate the drought potential of the classes within each factor. A cumulative map created through spatial join of all the three types of drought provided a drought hazard scenario in totality. The area with different severity of drought hazards under cumulative drought hazards scenario is about 40% of the area under high to very high drought scenario. It is revealed the immediate attention have made to groundwater development for sustainable environment in the study area.Keywords
Remote Sensing, GIS, Normalized Weight, Drought Hazard, Southern India.- Assessment of Land Use and Land Cover Changes in Magnesite Mining Region, Salem, India
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PDF Views:149
Authors
Affiliations
1 Centre for Geoinformatics and Planetary Studies, Department of Geology, Periyar University, Salem, IN
1 Centre for Geoinformatics and Planetary Studies, Department of Geology, Periyar University, Salem, IN
Source
International Journal of Earth Sciences and Engineering, Vol 9, No 3 (2016), Pagination: 898-905Abstract
The land use and land cover information in the form of maps and statistical derived data has dealt with spatial planning, management and utilization of land. Digital image processing techniques can explore the changes of land use within the specified time period. Change detection technique used to determine land use and land cover changes in the magnesite mining area situated in Salem in different time periods from 1992 to 2010. The Landsat TM multi-temporal satellite data for the year 1992, 2001 and 2010 were utilized in the change detection analysis. Supervised classification method was employed using the maximum likelihood procedure in ENVI 4.7 software. The land use and land cover changes were observed through spatial data classification, statistical operations through relative changes, change matrix and accuracy assessment. The findings clearly depict that the changes in land use and land cover are due to mining activity in the study area. The results obtained in this study for the stipulated year’s shows that the increase in the mining area +2.35 sq.km affects the net change value of area of scrub forest by +10.77 sq.km and built-up land by +24.21 sq.km, hence reducing the dense forest by -12.98 sq.km and agricultural land by -24.34 sq.km area respectively. The relative changes are assessed with the aid of the raster image in pixels and the net change domain pixels evaluation for the period of 1992 - 2010. Error matrixes are function of cross tabulation and assess the classification accuracy. The current study reveals an overall accuracy of 78.82%.Keywords
Change Detection, Land Use and Land Cover, Change Matrix, Magnesite Mines-Salem.- Artificial Neural Networks to Detect Facial Abnormalities through Cephalometric Radiography using Bjork Analysis
Abstract Views :251 |
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Authors
Affiliations
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, IN
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2391-2401Abstract
The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.Keywords
Bjork’s Analysis, Cephalometric Analysis, Back Propagation Neural Network, Generalized Regression Neural Network.References
- B.S. Iyyer, S.I. Bhalajhi and S.I. Bhalajhi, “Orthodontics: the Art and Science”, Arya Publisher, 2012.
- S. Anbazhagan and N. Kumarappan, “Binary classification of day-Ahead Deregulated Electricity Market Prices using Neural Networks”, Proceedings of IEEE International Conference on Power, pp. 1-5, 2012.
- S.N. Sivanandam and S.N. Deepa, “Introduction to Neural Networks using Matlab 6.0”, Tata McGraw-Hill Education, 2006.
- N. Guru, A. Dahiya and R. Navin, “Decision Support System for Heart Diseases Prediction using Neural Networks”, Delhi Business Review, Vol. 8, No. 1, pp. 1-6, 2007.
- T. Zrimec and I. Kononenko, “Feasibility Analysis of Machine Learning in Medical Diagnosis from Aura Images”, Consciousness and Physical Reality, Vol. 4, No. 4, pp.66-72, 1999.
- A. Pomi and F. Olivera, “BMC Medical Informatics and Decision Making, Context-Sensitive Auto Associative Memories as Expert Systems in Medical Diagnosis”, BMC Medical Informatics and Decision Making, Vol. 6, No. 1, pp. 1-8, 2006.
- L. A. Zadeh, “Biological Application of the Theory of Fuzzy Sets and Systems”, Proceedings of International Symposium on Biocybernetics of the Central Nervous System, pp. 199-206, 1969.
- S. Moein, S. A. Monadjemi and P. Moallem, “A Novel Fuzzy-Neural based Medical Diagnosis System”, International Journal of Biological & Medical Sciences, Vol. 4, No. 3, pp. 146-150, 2009.
- M. Kuramae, M.B. Magnani, E.M. Boeck and A.S. Lucato, “Jarabak’s Cephalometric Analysis of Brazilian Black Patients”, Brazilian Dental Journal, Vol. 18, No. 3, pp. 258-262, 2007.
- S. Munandar and M. D. Snow, “Cephalometric Analysis of Deutero‐Malay Indonesians”, Australian Dental Journal, Vol. 40, No. 6, pp. 381-388, 1995.
- F. V. Tenti, “Cephalometric Analysis as a Tool for Treatment Planning and Evaluation”, The European Journal of Orthodontics, Vol. 3, No. 4, pp. 241-245, 1981.
- A. Oria, E. Schellino, M. Massaglia and B. Fornengo, “A Comparative Evaluation of Steiner’s and McNamara’s Methods for Determining the Position of the Bone Bases”, Minerva Stomatologica, Vol. 40, No. 6, pp. 381-389, 1991.
- J.R. Jarabak and J.A. Fizzell, “Technique and Treatment with Light-Wire Edgewise Appliances”, St. Louis: The CV Mosby Company, 1972.
- A. Bjork, “Prediction of Mandibular Growth Rotation”, American Journal of Orthodontics, Vol. 55, No. 6, pp. 585-599, 1969.
- T.J. Hutton, S. Cunningham and P. Hammond, “An Evaluation of Active Shape Models for the Automatic Identification of Cephalometric Landmarks”, The European Journal of Orthodontics, Vol. 22, No. 5, pp. 499–508, 2000.
- B. Bidanda, S. Motavalli and Patterson, “On the Development of an Integrated Computer System for Cephalometric Analyses”, Journal of Medical Systems, Vol. 14, No. 1-2, pp. 1-6, 1990.
- R. Leonardi, D. Giordano, F. Maiorana and Spampinato, “Automatic Cephalometric Analysis: A Systematic Review”, The Angle Orthodontist, Vol. 78, No. 1, pp. 145-151, 2008.
- R. Martina, R. Teti and M. Musilli, “Neural Network based Identification of Typological Diagnosis through Cephalometric Techniques”, European Journal of Orthodontics, Vol. 3, No. 2, pp. 1-12, 2001.
- R. Martina, R. Tetib, D. D’Addona and G. Iodicea, “Neural Network Based System for Decision Making Support in Orthodontic Extractions”, Proceedings of Virtual International Conference on Intelligent Production Machines and Systems, pp. 235-240, 2006.
- N. M. Al-Jasser, “Cephalometric Evaluation for Saudi Population using the Downs and Steiner Analysis”, Journal of Contemporary Dental Practice, Vol. 6, No. 2, pp. 52-63, 2005.
- Q. Yan, H. Yan, F. Han, X. Wei and T. Zhu, “SVM-Based Decision Support System for Clinic Aided Tracheal Intubation Predication with Multiple Features”, Expert Systems with Applications, Vol. 36, No. 3, pp.6588-6592, 2009.
- D.N. Davis and D. Forsyth, “Knowledge-Based Cephalometric Analysis: A Comparison with Clinicians using Interactive Computer Methods”, Computers and Biomedical Research, Vol. 27, No. 3, pp. 210-228, 1994.
- T.P. Ortho, “Orthodontic Folder and Computerized Cephalometry”, Available at http://www.orthotp.com/EN/orthodontics.html, Accessed at 2007.
- J. M. Abe, N. R. Ortega, M. C. Mário and M. Del Santo, “Paraconsistent Artificial Neural Network: An Application in Cephalometric Analysis”, Proceedings of International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 716-723, 2005.
- A. L. Jacobson, “The role of Radiographic Cephalometry in Diagnosis and Treatment Planning”, Proceedings of Introduction to Radiographic Cephalometry”, pp. 1-13, 1985.
- P. K. Yen, “Identification of Landmarks in Cephalometric Radiographs”, The Angle Orthodontist, Vol. 30, No. 1, pp. 35-41, 1960.
- D. F. Specht, “A General Regression Neural Network”, IEEE Transactions on Neural Networks, Vol. 2, No. 6, pp. 568–576, 1991.
- S. Anbazhagan and N. Kumarappan, “A Neural Network approach to Day-Ahead Deregulated Electricity Market Prices Classification”, Electric Power Systems Research, Vol. 86, pp. 140-150, 2012.
- S. Anbazhagan, “Athens Seasonal Variation of Ground Resistance Prediction using Neural Networks”, ICTACT Journal on Soft Computing, Vol. 6, No. 1, pp. 1113-1116, 2015.
- N. O. Nawari, R. Liang and J. Nusairat, “Artificial Intelligence Techniques for the Design and Analysis of Deep Foundations”, Electronic Journal of Geotechnical Engineering, Vol. 4, No. 2, pp. 1–21, 1999.
- Application of Teaching Learning Based Optimization in Multilevel Image Thresholding
Abstract Views :177 |
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Authors
Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
1 Department of Electrical Engineering, Annamalai University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2413-2422Abstract
This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific, parameterless algorithm that does not require any algorithm-specific parameters to be controlled by maximizing the Kapur entropy of various classes for image thresholding. The proposed model is compared with recent algorithms to threshold the seven standard benchmark and three test images. The simulation results have higher fitness function values even with the increase of the threshold number with less computation time. The Jaccard measure values are close to 0.99.Keywords
Kapur’s Entropy, Multilevel Thresholding, Teaching Learning Based Optimization.References
- S. Dey, S. Bhattacharyya and U. Maulik, “Quantum Behaved Multi-Objective PSO and ACO Optimization for Multi-Level Thresholding”, Proceedings of International Conference on Computational Intelligence and Communication Networks, pp. 242-246, 2014.
- A.K. Bhandari, A. Kumar and G.K. Singh, “Tsallis Entropy based Multilevel Thresholding for Colored Satellite Image Segmentation using Evolutionary Algorithms”, Expert Systems with Applications, Vol. 42, No. 22, pp. 8707-8730, 2015.
- N. Otsu, “A Threshold Selection method from Gray-Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.
- J.N. Kapur, P.K. Sahoo and A.K. Wong, “A New Method for Gray-Level Picture Thresholding using the Entropy of the Histogram”, Computer Vision, Graphics, and Image Processing, Vol. 29, No. 3, pp. 273-285, 1985.
- L.K. Huang and M.J.J. Wang, “Image Thresholding by Minimizing the Measures of Fuzziness”, Pattern Recognition, Vol. 28, No. 1, pp. 41-51, 1995.
- Y. Qiao, Q. Hu, G. Qian, S. Luo and W.L. Nowinski, “Thresholding based on Variance and Intensity Contrast”, Pattern Recognition, Vol. 40, No. 2, pp. 596-608, 2007.
- X. Li, Z. Zhao and H.D. Cheng, “Fuzzy Entropy Threshold Approach to Breast Cancer Detection”, Information Sciences Applications, Vol. 4, No. 1, pp. 49-56, 1995.
- C.H. Li and P.K.S. Tam, “An Iterative Algorithm for Minimum Cross Entropy Thresholding”, Pattern Recognition Letters, Vol. 19, No. 8, pp. 771-776, 1998.
- K. Li and Z. Tan, “An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding”, IEEE Access, Vol. 7, pp. 165571-165582, 2019.
- J. Kittler and J. Illingworth, “Minimum Error Thresholding”, Pattern Recognition, Vol. 19, No. 1, pp. 41-47, 1986.
- S. Zarezadeh and M. Asadi, “Results on Residual Renyi Entropy of Order Statistics and Record Values”, Information Sciences, Vol. 180, No. 21, pp. 4195-4206, 2010.
- S. Ouadfel and A. Taleb-Ahmed, “Social Spiders Optimization and Flower Pollination Algorithm for Multilevel Image Thresholding: A Performance Study”, Expert Systems with Applications, Vol. 55, pp. 566-584, 2016.
- S.S. Pal, S. Kumar, K. Kashyap, Y. Choudhary and M. Bhattacharya, “Multi-Level Thresholding Segmentation Approach based on Spider Monkey Optimization Algorithm”, Proceedings of International Conference on Computer and Communication Technologies, pp. 273-287, 2016.
- M.A. El Aziz, A.A. Ewees and A.E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation”, Expert Systems with Applications, Vol. 83, pp. 242-256, 2017.
- A. K. M. Khairuzzaman, and S. Chaudhury, “Multilevel Thresholding using Grey Wolf Optimizer for Image Segmentation”, Expert Systems with Applications, Vol. 86, pp. 64-76, 2017.
- S. Kotte, R.K. Pullakura and S.K. Injeti, “Optimal Multilevel Thresholding Selection for Brain MRI Image Segmentation based on Adaptive Wind Driven Optimization”, Measurement, Vol. 130, pp. 340-361, 2018.
- K.B. Resma and M.S. Nair, “Multilevel Thresholding for Image Segmentation using Krill Herd Optimization Algorithm”, Journal of King Saud University-Computer and Information Sciences, Vol. 12, No, 3, pp. 1-13, 2018.
- M. Ahmadi, K. Kazemi, A. Aarabi, T. Niknam and M.S. Helfroush, “Image Segmentation using Multilevel Thresholding based on Modified Bird Mating Optimization”, Multimedia Tools and Applications, Vol. 78, No. 16, pp. 23003-23027, 2019.
- A. Sharma, R. Chaturvedi, S. Kumar and U.K. Dwivedi, “Multi-Level Image Thresholding based on Kapur and Tsallis Entropy using Firefly Algorithm”, Journal of Interdisciplinary Mathematics, Vol. 23, No. 2, pp. 563-571, 2020.
- M. Abdel-Basset, V. Chang and R. Mohamed, “A Novel Equilibrium Optimization Algorithm for Multi-Thresholding Image Segmentation Problems”, Neural Computing and Applications, Vol. 34, No. 1, pp.1-34, 2020.
- R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-Learning-Based Optimization: An Optimization Method for Continuous Non-Linear Large-Scale Problems”, Information Sciences, Vol. 183, No. 1, pp. 1-15, 2012.
- R.V. Rao, V.J. Savsani and J. Balic, “Teaching-Learning-Based Optimization Algorithm for Unconstrained and Constrained Real-Parameter Optimization Problems”, Engineering Optimization, Vol. 44, No. 12, pp. 1447-1462, 2012.
- R.V. Rao and V.D. Kalyankar, “Parameter Optimization of Machining Processes using a New Optimization Algorithm”, Materials and Manufacturing Processes, Vol. 27, No. 9, pp. 978-985, 2012.
- M.A. Elaziz, A.A. Ewees and A.E. Hassanien, “Hybrid Swarms Optimization based Image Segmentation”, Proceedings International Conference on Hybrid Soft Computing for Image Segmentation, pp. 1-21, 2016.