Archive

Abstract:

The constrained mechanical systems by quasi-coordinates are more universal than by generalized coordinates. In this paper, the motion equations of nonconservative singular mechanical systems by quasi-coordinates in phase space are studied. The regularization forms of Boltzmann-Hamel equations for general holonomic and nonholonomic singular mechanical systems are derived. The results show that the canonical equations expressed by quasi-coordinates and quasivelocities have a completely single structure, which do not depend on the constraints or not. The nonholonomic singular mechanical system is a natural extension of the general holonomic singular mechanical system.

Keywords:

Quasi coordinates,Singular mechanical systems,Canonical Equaitons,

Refference:

I. Dirac P. A. M., “Quantization of Singular systems”, Can J Math, vol.2, pp:122-129, 1950.
II. Dirac P. A. M., “Lecture on Quantum Mechanics”, Book, Yeshi-va, UniversityPress, pp:8-16, 1964.
III. Dong L. X., Liang J. H., “Lie Symmetries and Conserved Quantities of Nonholonomic SingularMechanical Systems in Terms of Quasicoordinates”, Jiangxi Science, vol.33, no.1, pp:61-65, 2015.
IV. Fasso F., Sansonetto N., “Conservation of ‘Moving’ Energy in Nonholonomic Systems with Affine Constraints and Integrability of Spheres on Rotating Surfaces”, Journal of Non-Linear Science, vol.26, no.2, pp:519-544 , 2016.
V. Fu J. L., Liu R. W., “Lie symmetries and conserved quantities of nonholonomic mechanical systems in quasi coordinates”, Journal of Mathematical Physics, vol.20, no.1, pp: 63-69, 2000.
VI. Jahromi A. F., Bhat R. B., Xie W. F., “Integrated ride and handling vehicle model using Lagrangian quasi-coordinates”, International Journal of Automotive Technolgy, vol.16, no.2, pp:239-251, 2015.
VII. Li A. M., “Canonical symmetry of nonholonomic constraint systems”, Journal of Wuhan Technical College of Communication, vol.15, no.3, pp:1-3, 2013.
VIII. Li Z. P., “Constrained Hamiltonian systems and their symmetry properties”, Book, Beijing University of Technology Press, pp:3-8, 1999.

IX. Li Z. P., Jiang J. H., “Symmetries in Constrained Canonical System”, Book, Science Press, pp:9-19, 2002.
X. Li Z. P., “Generalized Noether theorem of regular form of nonholonomic singular systems and its inverse theorem”, HuangHuai Journal, vol.3, no.1, pp:8-16, 1992.
XI. Li Z. P., “Noether theorem of regular form and its application”, Science Bulletin, vol.36, no12, pp:954-958, 1991.
XII. Li Z. P., “Classical and quantum symmetry properties of Constrained Systems”, Book, Beijing University of Technology Press, pp:32-46, 1993.
XIII. Luo S. K.,“Mei symmetries, Noether symmetries and Lie symmetries of Hamilton canonical equations of singular systems’, Acta phys Sinica, vol.53, no.1, pp: 5-11, 2004.
XIV. Mahmoudkhani S., “Dynamics of a mass-spring-beam with 0:1:1 internal resonance using the analytical and continuation method”, International Journal of Non-Linear Mechanics, vol.97, pp:48-67, 2017.
XV. Mei F. X., Zhu H. P., “Lie symmetries and conserved quantities of singular Lagrange systems”, Journal of Beijing Institute of Technology, vol.9, no.1, pp:11-14, 2000.
XVI. Mei F. X., “The application of Li Group and Lie algebra to the constrained mechanical system”, Book, Science Press, pp:126-137, 1999.
XVII. Mei F. X., “Analytical mechanics (2)”, Beijing Institute of Technology Press, Book, pp:145-162, 2013.
XVIII. Qiao Y. F., Zhao S. H., “Lie symmetric theorem and inverse theorem of generalized mechanical systems in quasi coordinates”, Acta phys Sinica, vol.50, no.1, pp:1-7, 2001.
XIX. Wang X. J., Zhao X. L., Fu J. L., “Noether symmetry of nonholonomic systems in quasi coordinates in phase space”, Journal of Henan Institute of Education, vol.13, no.2, pp:21-23, 2004.
XX. Xu Z. X., Mei F. X., “Unified symmetry of general holonomic systems under quasi coordinates”, Acta phys Sinica, vol.54, no.12, pp:5521-5524, 2005.
XXI. Zhang Y., Xue Y., “Lie symmetry constraint Hamilton systems with the second type of constraints”, Acta phys Sinica, vol.50, no.5, pp:816-819, 2001.

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Abstract:

In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is considered as one of the major algorithms for intrusion detection. One of the major problems is the time of training and the need to improve its efficiency when it comes to work with large dimensions. In this research, we try to study a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction. Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised in this study is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. In this research, the network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. The various steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation. The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm. And also the KNN algorithm has been compared in situations with and without diminishing dimensions and the results have shown that the proposed method has a better performance than comparable methods.

Keywords:

intrusion detection rate,computer networks,SVM,

Refference:

I. Alesh Kumar Sharma, Satyam Maheswari. Network Intrusion detection by using PCA via SMO-SVM. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE). Volume 1, Issue 10, 2012.
II. Anke Meyer-Baese and Volker Schmid. Feature Selection and Extraction, In Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition), edited by Anke Meyer-Baese and Volker Schmid, Academic Press, Oxford, Pages 21-69, ISBN 9780124095458, 2014.
III. Azencott, Robert, et al. “Automatic clustering in large sets of time series.” Contributions to Partial Differential Equations and Applications. Springer, Cham, 65-75, 2019.

IV. Baghban, Alireza, et al. “Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction.” Petroleum Science and Technology.1-6, 2018.
V. Gao, Junbin, Qinfeng Shi, and Tibero S. Caetano. “ Dimensionality reduction via compressive sensing,” Pattern Recognition Letters 33.9,1163-1170, 2012.
VI. Gunupudi Rajesh Kumar, Nimmala Mangathayaru and Gugulothu Narsimha. A feature clustering based Dimensionality reduction for intrusion Detection (FCBDR). IADIS International Journal on Computer Science and Information Systems. 12(1), 26-44, 2017.
VII. H. Om and A. Kunda, “A Hybrid System For Reducing the False Alarm Rate of Anomaly Intrusion Detection System”, in International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, 2012.
VIII. Hekmati, R., Azencott, R., Zhang, W., Paldino, M. “Localization of Epileptic Seizure Focus by Computerized Analysis of fMRI Recordings”.arXiv, 2018.
IX. Hekmati, R., et al. “Machine Learning to Evaluate fMRI Recordings of Brain Activity in Epileptic Patients, 2015.
X. Hekmati, Rasoul. “On efficiency of non-monotone adaptive trust region and scaled trust region methods in solving nonlinear systems of equations.” Biquarterly Control and Optimization in applied Mathematics 1.1, 31-40, 2016.
XI. Hyunsoo Kim, Peg Howland and Haesun Park.Dimension Reduction in Text Classification with Support Vector Machines. The Journal of Machine Learning Research archive. Volume 6, 12/1/2005. Pages 37-53, 2005.
XII. I. Ahmad, M. Hussain, A. Alghamdi, A. Alelaiwi, “Enhancing SVM Performance In Intrusion Detection Using Optimal Feature Subset Selection Based on Genetic Principal Components”, Neural Computing and
Applications, vol. 24, no. 7-8, pp. 1671-1682, 2014.
XIII. J.Shen and S. Mousavi, ”Least sparsity of p-norm based optimization problems with p>1, ” arXiv preprint arXiv:1708.06055, 2017.
XIV. Li Y, Qiu R, Jing S. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid. PLoSONE 13(2), 66-79, 2018.
XV. Luxburg U. V., Bousquet O., “Distance–based classification with Lipschitz functions”, Journal of Machine Learning Research, Vol. 5, pp. 669-695, 2004.
XVI. M. Hasan, M. Nasser, B. Pal, “Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)”, Journal of Intelligent Learning Systems and Applications, vol. 6, no. 1, 2014.
XVII. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” in Proceeding of the 2009 IEEE symposium on computational Intelligence in security and defense application (CISDA), 2009.
XVIII. Mingyu Fan, Nannan Gu, Hong Qiao, Bo Zhang, Dimensionality reduction: An interpretation from manifold regularization perspective, Information Sciences, Volume 277, 1, 694-714, ISSN 0020-0255, 2014.

XIX. N. Revathy and R. Balasubramanian, “GA-SVM wrapper approach for gene ranking and classification using expressions of very few genes,” Journal of Theoretical and Applied Information Technology, vol. 40, no. 2, pp. 113–119, 2012.
XX. Najarian, M., et al. “Evolutionary Vertical Size Reduction: A Novel Approach for Big Data Computing”. International Journal of Mathematics and its Applications, 2018. XXI. NSL-KDD data set for network-based intrusion detection systems.” Available on: http://nsl.cs.unb.ca/NSL-KDD/, 2009.
XXII. R. Lippmann, J. Haines, D. Fried, J. Korba, and K. Das, “The 1999 DARPA off-line intrusion detection evaluation,” Computer Networks, 34, pp.579-595, 2000.
XXIII. R. Ravinder Reddy; Y Ramadevi ; K. V. N Sunitha. Effective discriminant function for intrusion detection using SVM. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). DOI: 10.1109/ICACCI.2016.7732199, 2016.
XXIV. S. Ahmadian, H Malki, AR Sadat , “Modeling Time of Use Pricing for Load Aggregators Using New Mathematical Programming with Equality Constraints”, 5th International Conference on Control, Decision, 2018.
XXV. S. J. Stolfo, W. Fan, A. Prodromidis, P. K. Chan, W. Lee, “Cost-sensitive modeling for fraud and intrusion detection: Results from the JAM project”, in Proceedings of the 2000 DARPA Information Survivability Conference and Exposition, 2000.
XXVI. S. Maldonado, R. Weber, and J. Basak, “Simultaneous feature selection and classification using kernel-penalized support vector machines,” Information Sciences, vol. 181, no. 1, pp. 115–128, 2011.
XXVII. Sebastián Maldonado, Juan Pérez, Richard Weber, Martine Labbé, Feature selection for Support Vector Machines via Mixed Integer Linear Programming, Information Sciences, Volume 279, 20, Pages 163-175, 2014.
XXVIII. Vinodhini G., Chandrasekaran R.M. Sentiment Mining Using SVM-Based Hybrid Classification Model. In: Krishnan G., Anitha R., Lekshmi R., Kumar M., Bonato A., Graña M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246, 2014.
XXIX. Vinodhini G., Chandrasekaran R.M. Sentiment Mining Using SVM-Based Hybrid Classification Model. In: Krishnan G., Anitha R., Lekshmi R., Kumar M., Bonato A., Graña M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246. Springer, New Delhi, 2014.
XXX. Xintao Qiu, Dongmei Fu and Zhenduo Fu.An Efficient Dimensionality Reduction Approach for Small-sample Size and High-dimensional Data Modeling. journal of computers, vol. 9, no. 3, march, 2014.
XXXI. Zena M. Hira and Duncan F. Gillies (2015). A Review of Feature Se lection and Feature Extraction Methods Applied on Microarray Data. Advances in Bioinformatics, 2015.

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Abstract:

Initial identification about pancreatic cancer (PC) will be a very challenging task due to particular symptoms of cancer happens only at an advanced phase & a dependable screening device to detect high danger patients. To know this challenge, a new method for decreasing the features might have been developed, tested & trained with the use of the health information of 800,114 defendants caught in the “national health interview survey (NHIS)”& Pancreatic, Colorectal, Lung, & “PLCO (ovarian cancer)” datasets, together risk of cancer might have been evaluated at a distinct level by including 18 characteristics under the recommended. The recognized “hybrid feature selection method” attained a true positive rate of 87.3 & 80.7% a true negative rate 0.86 & 0.85 for the training and testing associates, individually.

Keywords:

American Cancer Society (ACS),Machine learning (ML),Feature selection (FS),Feature extraction (FE),pancreatic cancer (PC),

Refference:

I. American Cancer Society (2017). Cancer Facts & Figures 2017. Atlanta, GA: American Cancer Society.
II. Arslan, A. A., Helzlsouer, K. J., Kooperberg, C., Shu, X.-O., Steplowski, E., Bueno-De-Mesquita, H. B., et al. (2010). Anthropometric measures, body mass index, and pancreatic cancer: a pooled analysis from the Pancreatic Cancer Cohort Consortium (PanScan). Arch. Intern. Med. 170, 791–802. doi:
10.1001/archinternmed.2010.6.
III. Association, A. D. (2014). Diagnosis and classification of diabetes mellitus. Diabetes Care. 37, S81–S90. doi: 10.2337/dc10-S062
IV. Bakpo, F., and Kabari, L. (2011). “Diagnosing skin diseases using an artificial neural network,” in Artificial Neural Networks-Methodological Advances and Biomedical Applications, ed K. Suzuki (InTech), 253–270.
V. Ben, Q., Xu, M., Ning, X., Liu, J., Hong, S., Huang, W., et al. (2011). Diabetes mellitus and risk of pancreatic cancer: a meta-analysis of cohort studies. Eur. J. Cancer. 47, 1928–1937. doi: 10.1016/j.ejca.2011.03.003
VI. Blewett, L. A., Rivera Drew, J. A., Griffin, R., King, M. L., and Williams, K.C. W. (2017). IPUMS Health Surveys: National Health Interview Survey, Version 6.2 [dataset]. Minneapolis, MN: University of Minnesota.
VII. Boursi, B., Finkelman, B., Giantonio, B. J., Haynes, K., Rustgi, A. K., Rhim, A. D., et al. (2017). A clinical prediction model to assess risk for pancreatic cancer among patients with new-onset diabetes. Gastroenterology. 152, 840 – 850.e843. doi: 10.1053/j.gastro.2016.11.046
VIII. Boursi, S. B., Finkelman, B., Giantonio, B. J., Haynes, K., Rustgi, A. K., Rhim, A., et al. (2018). A clinical prediction model to assess risk for pancreatic cancer among patients with pre-diabetes. J. Clin. Oncol. 36(15_Suppl.). doi: 10.1200/JCO.2018.36.15_suppl.e16226
IX. Iodice, S., Gandini, S., Maisonneuve, P., and Lowenfels, A. B. (2008). Tobacco and the risk of pancreatic cancer: a review and meta-analysis. Langenbecks Arch. Surg. 393, 535–545. doi: 10.1007/s00423-007-0266-2
X. Klein, A. P., Lindström, S., Mendelsohn, J. B., Steplowski, E., Arslan, A. A., Bueno-De-Mesquita, H. B., et al. (2013). An absolute risk model to identify individuals at elevated risk for pancreatic cancer in the general population. PLoS ONE. 8:e72311. doi: 10.1371/journal.pone.0072311.
XI. Lucenteforte, E., La Vecchia, C., Silverman, D., Petersen, G., Bracci, P., Ji, B. A., et al. (2011). Alcohol consumption and pancreatic cancer: a pooled analysis in the International Pancreatic Cancer Case–Control Consortium (PanC4). Ann. Oncol. 23, 374–382. doi: 10.1093/annonc/mdr120.
XII. Michaud, D. S., Vrieling, A., Jiao, L., Mendelsohn, J. B., Steplowski, E., Lynch, S. M., et al. (2010). Alcohol intake and pancreatic cancer: a pooled analysis from the pancreatic cancer cohort consortium (PanScan). Cancer Causes Control. 21, 1213–1225. doi: 10.1007/s10552-010-9548-z.
XIII. Poley, J. W., Kluijt, I., Gouma, D. J., Harinck, F., Wagner, A., Aalfs, C., et al. (2009). The yield of first-time endoscopic ultrasonography in screening individuals at a high risk of developing pancreatic cancer. Am. J. Gastroenterol. 104:2175–2181. doi: 10.1038/ajg.2009.276
XIV. Pannala, R., Basu, A., Petersen, G. M., and Chari, S. T. (2009). New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer. Lancet Oncol. 10, 88–95. doi: 10.1016/S1470-2045(08)70337-1
XV. Pannala, R., Leirness, J. B., Bamlet, W. R., Basu, A., Petersen, G. M., and Chari, S. T. (2008). Prevalence and clinical profile of pancreatic cancer– associated diabetes mellitus. Gastroenterology. 134, 981–987. doi:10.1053/j.gastro.2008.01.039

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Abstract:

Most of failures in the friction clutches occur due to the excessive heat generated due to friction between various parts, and this heat causes high temperatures leading to high thermal stresses. In the present research paper, numerical simulation had been developed using finite element method to simulate the thermal behavior of the dry friction clutch. Three-dimensional finite element model was made and analyzed using ANSYS/Workbench sofware18. The friction clutch system was firstly modeled mathematically and solved numerically to determine the transient thermal response of the clutch disc. The two fundamental methods of uniform wear and uniform pressure are assumed. The applied torque during the sliding period was constant. The temperature and heat generated were estimated for each clutch part (pressure plate, clutch disc and flywheel) using heat partition ratio. The assumptions that are inherent in the derivation of the governing equations are presented which followed up by the appropriate boundary conditions. The results show that the maximum temperature values for uniform pressure condition are greater than those for uniform wear condition. Also, the temperature value increased with time and approximately reaches the highest value at the middle of the sliding period when the applied torque is constant with time and then decreased to the final values at the end of slipping period.

Keywords:

Dry friction clutch,thermal analysis,3D FEM,

Refference:

I. Balázs Czél, Károly Váradi, Albert Albers, and Michael Mitariu, “Fe thermal analysis of aceramic clutch”, Journal of Tribology International, 42(5):714–723, 2009.
II. Belhocine, Ali, and Mostefa Bouchetara, “Thermomechanical modeling of dry contacts in automotive disc brake”, International Journal of Thermal Sciences 60:161-170, 2012.
III. Choon Yeol Lee, Il Sup Chung, and Young Suck Chai, “Finite element analysis of an automobile clutch system”, Journal Key Eng. Materials, 353-358:2707-2711, 2007.
IV. E. Mouffak, M. Bouchetara, “Transient thermal behavior of automotive dry clutch discs by using Ansys software”, ISSN 1392-1207. MECHANIKA, Vol. 22, No. 6, pp. 562−570 (2016).
V. Faramarz Talati , Salman Jalalifar, “Analysis of heat conduction in a disk brake system”, jornal of Heat Mass Transfer 45:1047–1059, 2009.
VI. Jenan S. Sherza, Ihsan Y. Hussain, Oday I. Abdullah. “Heat flux in friction clutch with time dependent torque and angular velocity”, International Conference on Advanced Science and Engineering (ICOASE), 2018.
VII. Oday I. Abdullah, J. Schlattmann, “Effect of band contact on the temperature distribution for dry friction clutch”, World Acad. Sci., Eng. and Technol., Int. Sci. Index 6.9 (2012): 150-160.
VIII. Oday I. Abdullah, Josef Schlattmann , “computation of surface temperatures and energy dissipation in dry friction clutch for varying torque with time”, International Journal of Automotive Technology, Vol.
15, No. 5, pp. 733−740 (2014).
IX. Oday I. Abdullah, Josef Schlattmann , “Thermal behavior of friction clutch disc based on uniform pressure and uniform wear assumptions”, Friction 4(3): 228–237 (2016).
X. Oday I. Abdullah, Josef Schlattmann , “Thermal behavior of friction clutch disc based on uniform pressure and uniform wear assumptions”, FME Transactions vol.46, pp.33–38 (2018).
XI. Yevtushenko, Kuciej A. A., M., and Yevtushenko O., “Three-element model of frictional heating during braking with contact thermal resistance and time-dependent pressure”, International Journal of Thermal Sciences, 50(6):1116-1124, 2011.
XII. Yogesh Emeerith, Dr. Rabindra Nath Barman, “Structural and Thermal Analysis of a Single Plate Dry Friction Clutch Using Finite Element Method (Fem)” IDL – International Digital Library Of Technology &
Research, volume 1, Issue 5, May (2017).

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Abstract:

As the world thrives for power in order to strengthen its industrial demands and economy, traditional power sources are becoming more and more difficult to fulfill the rising demands. Renewable energy demand in the world whether third world countries or leading ones of the era, has seen a boost in recent decades. Photovoltaic and solar energy is an ongoing trend in power system designers, researchers and companies. As sun is the free source of energy, the world now a days achieves 30% of its total energy from it. Solar power is sporadic and is not constant, as solar source at the ground level is extremely reliant on clouds density, atmospheric conditions with other restrictions. These limitations become a challenging task for engineers and energy managers to focus the energy constraints and came up with managing plan in order to produce and manage energy efficiently in smart grids. This paper focuses on energy constraints of both solar resource and PV power alongside smart grid energy management.

Keywords:

Solar energy,PV cells, energy forecast,smart grid management,

Refference:

I. A. Sfetsos and A. H. Coonick, “Univariate and multivariate forecasting of
hourly solar radiation with artificial intelligence techniques,” Solar Energy,
vol. 68, no. 2, pp. 169–178, Feb. 2000.
II. B. Kumar Sahu, “A study on global solar PV energy developments and
policies with special focus on the top ten solar PV power producing
countries,” Renewable & Sustainable Energy Reviews, vol. 43, no. 0, pp.
621–634, Mar. 2015.
III. D. Yang, C. Gu, Z. Dong, P. Jirutitijaroen, N. Chen, and W. M. Walsh,
“Solar irradiance forecasting using spatial-temporal covariance structures
and time-forward kriging,” Renewable Energy, vol. 60, no. 0, pp. 235–245,
Dec. 2013.
IV. E. Geraldi, F. Romano, and E. Ricciardelli, “An advanced model for the
estimation of the surface solar irradiance under all atmospheric conditions
using MSG/SEVIRI data,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 50, no. 8, pp. 2934–2953, Aug. 2012.
V. F. Bizzarri, M. Bongiorno, A. Brambilla, G. Gruosso, and G. S. Gajani,
“Model of photovoltaic power plants for performance analysis and
production forecast,” IEEE Transactions on Sustainable Energy, vol. 4, no.
2, pp. 278–285, Apr. 2013.
VI. F. Ueckerdt, R. Brecha, and G. Luderer, “Analyzing major challenges of
wind and solar variability in power systems,” Renewable Energy, vol. 81,
no. 0, pp. 1–10, Sep. 2015.
VII. H.-T. Yang, C.-M. Huang, Y.-C. Huang, and Y.-S. Pai, “A weather-based
hybrid method for 1-day ahead hourly forecasting of PV power output,”
IEEE Transactions on Sustainable Energy, vol. 5, no. 3, pp. 917–926, Jul.
2014.
VIII. IEA-PVPS, “Annual Report 2014 (AR2014),” May 2015.
IX. M. Hosenuzzaman, N. A. Rahim, J. Selvaraj, M. Hasanuzzaman, A. B. M.
A. Malek, and A. Nahar, “Global prospects, progress, policies, and
environmental impact of solar photovoltaic power generation,” Renewable
& Sustainable Energy Reviews, vol. 41, no. 0, pp. 284–297, Jan. 2015.

X. P. Bacher, H. Madsen, and H. A. Nielsen, “Online short-term solar power
forecasting,” Solar Energy, vol. 83, no. 10, pp. 1772–1783, Oct. 2009.
XI. R. G. Wandhare and V. Agarwal, “Reactive power capacity enhancement of
a PV-grid system to increase PV penetration level in smart grid scenario,”
IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1845– 1854, Jul. 2014.
XII. R. Shah, N. Mithulananthan, R. C. Bansal, and V. K. Ramachandaramurthy,
“A review of key power system stability challenges for largescale PV
integration,” Renewable & Sustainable Energy Reviews, vol. 41, no. 0, pp.
1423–1436, Jan. 2015.
XIII. R. Perez, E. Lorenz, S. Pelland, M. Beauharnois, G. Van Knowe, K.
Hemker Jr, et al., “Comparison of numerical weather prediction solar
irradiance forecasts in the US, Canada and Europe,” Solar Energy, vol. 94,
no. 0, pp. 305–326, Aug. 2013.
XIV. R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr, D. Renn´e, and T. E.
Hoff, “Validation of short and medium term operational solar radiation
forecasts in the US,” Solar Energy, vol. 84, no. 12, pp. 2161–2172, Dec.
2010.
XV. S. J. Steffel, P. R. Caroselli, A. M. Dinkel, J. Q. Liu, R. N. Sackey, and N.
R. Vadhar, “Integrating solar generation on the electric distribution grid,”
IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 878–886, Jun. 2012.
XVI. S. D. Campbell and F. X. Diebold, “Weather forecasting for weather
derivatives,” Journal of the American Statistical Association, vol. 100, no.
469, pp. 6–16, Mar. 2005.
XVII. S. I. Nanou, A. G. Papakonstantinou, and S. A. Papathanassiou, “A generic
model of two-stage grid-connected PV systems with primary frequency
response and inertia emulation,” Electric Power Systems Research, vol.
127, no. 0, pp. 186–196, Oct. 2015.
XVIII. S. Eftekharnejad, G. T. Heydt, and V. Vittal, “Optimal generation dispatch
with high penetration of photovoltaic generation,” IEEE Transactions on
Sustainable Energy, vol. 6, no. 3, pp. 1013–1020, Jul. 2015.
XIX. T. Sueyoshi and M. Goto, “Photovoltaic power stations in Germany and the
United States: A comparative study by data envelopment analysis?” Energy
Economics, vol. 42, no. 0, pp. 271–288, Mar. 2014.
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power point tracking techniques,” IEEE Transactions on Energy
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XXI. Z. Dong, D. Yang, T. Reindl, and W. M. Walsh, “Satellite image analysis
and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics,”
Energy Conversion and Management, vol. 79, no. 0, pp. 66–73, Mar. 2014.
XXII. Z.-Y. Zhao, S.-Y. Zhang, B. Hubbard, and X. Yao, “The emergence of the
solar photovoltaic power industry in China,” Renewable & Sustainable
Energy Reviews, vol. 21, no. 0, pp. 229–236, May 2013.

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Abstract:

In this paper we have studied the α -ideals in a 0-distributive lattice. We have described the α -ideals by two definition and proved that these are equivalent. We have given several characterizations. They have proved that a lattice L is disjunctive if and only if each ideal is an α -ideals. We have also included a prime separation theorem for α -ideals. At the end we have studied the α -ideals in a sectionally quasi-complemented lattice.

Keywords:

α -ideals,0-distributive lattice,separation theorem,quasicomplemented lattice,

Refference:

I. Ayub Ali, Noor, A. S. A. and Islam, A. K. M. S. Annulets in a
Distributive Nearlattice; Annals of Pure and Applied Mathematics, Vol.
3, No. 1, (2012), 91-96.
II. Ayub Ali, R. M. Hafizur Rahman and A. S. A. Noor; Prime Separation
Theorem for α – ideals in a 0-distributive Lattice; Journal of Pure and
Applied Science, Assam, India. 12(1) (2012), pp. 16-20.
III. Ayub Ali, R. M. Hafizur Rahman & A. S. A. Noor; On Semi prime n –
ideals in Lattices; Annals of Pure and Applied Mathematics. Vol. 2, No.-
1, Page: 10-17 (2012).
IV. Md. Ayub Ali, R. M. Hafizur Rahman, A. S. A. Noor & Jahanara Begum;
Some characterization of n -distributive lattices; Institute of Mechanics of
Continua and Mathematical Sciences, Township, Madhyamgram, Kolkata-
700129, Volume-7, Number-2, Page: 1045-1055 (2013).
V. Cornish, W. H., Annulets and α -ideals in a distributive lattice; J.
Aust. Math. Soc. 15(1) (1975), 70-77.
VI. Jaidur Rahman, A study on 0-distributive near lattice; Ph. D Thesis,
Khulna university of Engineering and Technology.
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Abstract:

Earthquake is one of the most important natural phenomena and humans have always been trying to control its adverse effects. In the past century, the development of cities and the high investment in them and many financial and life losses caused by earthquake and, on the other hand, the ever-increasing advances in science and technology that allow for more accurate knowledge of the factors causing the earthquake and how to control it have made humans reduce its financial and life losses by making suitable and earthquake resistant structures. Today, due to the increasing growth of terrorist activities, the risk of structures facing blast loads has also increased. The occurrence of various terrorist incidents in relation to important structures around the world has caused that in recent years, blast loads become the focus of special attention. This article examines the connection of steel structures with honeycomb damper by applying blast and earthquake loads in Abaqus finite element software. Three frame models with 6, 9 and 13 floors have been considered for the study. For air blast, 10 Kg of TNT have been used. To apply earthquake records, seven pairs of accelerograms have been employed. By examining the results of numerical modeling in Abaqus finite element software, it can be observed that as a result of applying blast load, the damper could not react. But due to applying earthquake records, the damper’s behavior was very good so that at the beamcolumn joint, the highest amount of stress was created in the damper. Considering that applying the blast loading occurs in less than a few milliseconds and the structure does not have enough time to react to this load, blast load failure has been local and sectional.

Keywords:

Blast,honeycomb damper,Abaqus,moment frame,

Refference:

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Abstract:

Alcoholism could be identified through analyzing electroencephalogram (EEG) signals. Yet, it is difficult to analyze with multi-channel EEG signal since it is frequently needing long time for execution and complex calculations. The presented paper proposed 13 optimal channel to feature extraction. Firstly, 1200 recordings of biomedical signals will be presented for extracting the sample entropy. Statistical analysis approach will be utilized for the purpose of choosing the best channels for identifying abnormalities in alcoholics. Secondly four classifiers are applied at the decision level, Naïve Bayes, SVM, Logistic Regression, KNN, the accuracy was 80.1%,92.5%, 73.7% and 90.3%Respectively, in this study the SVM classifier is more accuracy .

Keywords:

EEG signal,optimal channel,abnormalities in alcoholics,SVM classifier,

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Abstract:

Currently, hundreds of consumer products in cludelarge-scale nanoparticles; this enhances the possibility of such particles to be released into water and in result causesenvironmental and human health issues. In this research, asynthesis of PolyMethylMethAcrylate (PMMA) nano-membrane for the filtration of nanoparticles from natural water is demonstrated. Electrospinning technique is deployed for the synthesis of PMMA nanofibers. The synthesized nanofibers are further optimized by adding Di-Methyl Formamide (DMF) and acetone that provides elasticity and increases the exterior area of the nano-membranes. The resultant membrane is tendbal and instinctivelyrobust enough to resist filtration under high stress. The synthesized nanofibers are further analyzed and characterized by using spectroscopy (UV-Vis), Fourier Transform Infra-Red spectroscopy (FTIR) and Scanning Electron Microscope(SEM).The SEM, UV-vis and FTIR result shows the filtration rate of the fabricated membrane could capably exclude nanoparticles with different sizes (from 10 to 100 nm in diameter) from a feed solution.

Keywords:

Electrospinning,Fiber diameter,FTIR,SEM,Water filtration,

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Abstract:

The space is filled with Higgs fields. As other fields like electric fields or magnetic fields, Higgs fields are of elliptical shape. Higgs fields are small individual fields represent a tiny part of space. Every adjacent Higgs Fields have opposite rotations, the repulsive force makes them to have unique identity for themselves which makes free space highly stable. Opposite rotation is the reason that any two Higgs fields do not mingle with each other to form larger field in free space, but under the influence of ordinary matter like Earth, Higgs fields change their orientations to be unidirectional to form a larger field called gravity. The larger the mass, the higher the number of Higgs fields to have unidirectional orientations. The force carrying particle Higgs Boson is responsible for Higgs field and the force carrying particle graviton is responsible for gravitational force. The unidirectional orientations of many small oval shaped Higgs Boson yields the graviton which has oval shape too. Thus, Higgs Boson and graviton are same force carrying particle acting differently at different situations. For example, stationary charge gives electric field where as moving charge gives magnetic field. The phenomenon of both is basically the same but looks different due to movement. Maxwell realized that the phenomenon of both is the same with the same force carrying particle but act differently at different situations. In this paper we will find, why wave propagations of waves are wavy. We will also find why the shape and orbit of astronomical objects are of similar pattern – elliptical or oval shape.

Keywords:

Astronomical Objects,Higgs Field,Graviton,Higgs Boson,Force Carrying Particle,

Refference:

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