Journal Vol – 14 No -6, December 2019

Abstract:

In the present era, we have a new method called cloud computing, in which the services are shared over the Internet. There are many organizations that provide cloud processing services. Cloud processing allows users and developers to use these services without interfering with the technical knowledge or control of the technology they require, but on the other hand, day to day, more information of individuals and companies is stored inside clouds, which puts the challenge of data security ahead of users. Information security in virtual environments and new area of cloud computing has always been emphasized as one of the basic infrastructures and essential requirements for ICT-intensive use. Although absolute security is unattainable both in the real environment and in the virtual environment, it is possible to create a level of security that is sufficiently adequate in almost all environmental conditions. In cloud computing, there are many security challenges that must be addressed by cloud service providers to convince users to use this technology. One of the most important issues is ensuring the user's data is inaccurate and unavailable. For the user, the security process used to store data in the cloud is very obscure, long, and vague. In this research, a security approach based on abnormal behavior is designed to detect events that are unusual and abnormal in relation to other system behaviors. The focus of this paper is the use of evolutionary algorithms such as genetics or other new algorithms such as an imperialist competitive algorithm to detect these abnormal behaviors with intelligence agents. In similar studies, various optimization methods such as genetic algorithms, pso have been used. The proposed algorithm can be compared and evaluated with previous methods.

Keywords:

abnormal behavior,security,cloud computing,evolutionary algorithm,imperialist competitive algorithm,

Refference:

I. A. Vakili, N.J. Navimipour. Comprehensive and systematic review of the
service composition mechanisms in the cloud environments. J. Netw.
Comput. Appl., 81 pp. 24-36, 2017.
II. B. Wang, Y. Zheng, W. Lou, and Y. T. Hou, “DDoS attack protection in the
era of cloud computing and software-defined networking,” Computer
Networks, vol. 81, pp. 308-319, 2015.
III. Beiter, Michael, et al. “End-to-end policy based encryption techniques for
multi-party data management.” Computer Standards & Interfaces, 36.4 689-
703, 2014.
IV. Bose, Ranjit, Xin Luo, and Yuan Liu. “The Roles of Security and Trust:
Comparing Cloud Computing and Banking.” Procedia-Social and Behavioral
Sciences, 73: 30-34, 2013.
V. Che, Jianhua, et al. “Study on the security models and strategies of cloud
computing.” Procedia Engineering , 23: 586-593, 2011.
VI. Dastjerdi, Amir Vahid, Kamalrulnizam Abu Bakar, and Sayed Gholam
Hassan Tabatabaei. “Distributed intrusion detection in clouds using mobile
agents.” Advanced Engineering Computing and Applications in Sciences,
2009. ADVCOMP’09. Third International Conference on. IEEE, 2009.
VII. Govinda, K., and E. Sathiyamoorthy. “Agent Based Security for Cloud
Computing using Obfuscation.” Procedia Engineering 38 (2012): 125-129.
VIII. Jalal Nouri,D.,Saniee Abadeh, M.&Ghareh Mohammadi, F. HYEI: A New
Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy
Knowledge Discovery. Advances in Fuzzy Systems. vol. 14, no. 11, 387-395,
2014.
IX. Jin, Hai, et al. “A guest-transparent file integrity monitoring method in
virtualization environment.” Computers & Mathematics with Applications,
60, 2: 256-266, 2010.
X. K. Vieira, A. Schulter, C. Westphall, and C. Westphall, “Intrusion detection
techniques in grid and cloud computing environment,” IT Professional, IEEE
Computer Society, vol. 12, pp. 38-43, 2010.
XI. Kim, Junghan, Inhyuk Kim, and Young Ik Eom. “Nopfit: File system
integrity tool for virtual machine using multi-byte nop injection.”
Computational Science and Its Applications (ICCSA), 2010 International
Conference on. IEEE, 2010.
XII. Kong, Jinzhu, AdjointVM: a new intrusion detection model for cloud
computing. Elsevier, 2011.
XIII. Kousiouris, George, et al. “Dynamic, behavioral-based estimation of resource
provisioning based on high-level application terms in Cloud platforms.”
Future Generation Computer Systems, 32: 27-40, 2014.
XIV. Kshetri, Nir. “Privacy and security issues in cloud computing: The role of
institutions and institutional evolution.” Telecommunications Policy, 37, 4:
372-386, 2013.
XV. Lai, Yung-Liang. “Analyzing strategies of mobile agents on malicious cloud
platform with Agent-Based Computational Economic Approach.” Expert
Systems with Applications, 40, 7: 2615-2620, 2013..

XVI. Lo, Chi-Chun, Chun-Chieh Huang, and Joy Ku. “A cooperative intrusion
detection system framework for cloud computing networks.” Parallel
processing workshops (ICPPW), 2010 39th international conference on.
IEEE, 2010.
XVII. M. Haresh, S. Kalady, and V. Govindan, “Agent based dynamic resource
allocation on federated clouds,” in Recent Advances in Intelligent
Computational Systems (RAICS), IEEE, 2011, pp. 111-114, 2011.
XVIII. M. Tang, X. Dai, J. Liu, J. Chen. Towards a trust evaluation middleware for
cloud service selection. Future Gener. Comput. Syst., 74, pp. 302-312, 2017.
XIX. Ms. Parag K. Shelke, Ms. Sneha Sontakke, Dr.A. D. Gawande, Intrusion
Detection System for Cloud Computing. International Journal of Scientific &
Technology Research, 2012.
XX. N. Dot. Anomaly Detection via Online Over-Sampling Principal Component
Analysis| FTJ1314, 2015.
XXI. N. Ghadiri, A. Baraani-Dastjerdi, N. Ghasem-Aghaee, and M. A.
Nematbakhsh, “Optimizing the performance and robustness of type-2 fuzzy
group nearest-neighbor queries,” Mobile Information Systems, vol. 7, pp.
123-145, 2011.
XXII. Pearson Siani. Toward accountability in the cloud. EEE Internet
Comput;15(4):64–99, 2011.
XXIII. R.Roustaei and F.Yousefi Fakhr. A Hybrid Meta-Heuristic Algorithm based
on Imperialist Competition Algorithm. Journal of AIand Data Mining Vol 6,
No 1, 59-67, 2018.
XXIV. Rasheed, Hassan. “Data and infrastructure security auditing in cloud
computing environments.” International Journal of Information Management
34.3: 364-368, 2014.
XXV. Rong Chunming, Nguyen Son T. Cloud trends and security challenges. In:
Proceedings of the 3rd international workshop on security and computer
networks (IWSCN 2011), 2011.
XXVI. Shin, Dong-Hee. “User centric cloud service model in public sectors: policy
implications of cloud services.” Government Information Quarterly 30.2:194-
203, 2013.
XXVII. Sood, Sandeep K. “A combined approach to ensure data security in cloud
computing.” Journal of Network and Computer Applications 35.6, 1831-
1838, 2012.
XXVIII. T. Garfinkel and M. Rosenblum, “A Virtual Machine Introspection Based
Architecture for Intrusion Detection,” in Ndss, pp. 191-206, 2003.
XXIX. Tarigonda, Siva, A. Ganesh, and Srinivasulu Asadi. “Providing Data Security
in Cloud Computing using Novel and Mixed Agent based Approach.”
International Journal of Computer Applications 112.6, 2015.
XXX. Tianfield, Huaglory. “Cloud computing architectures.” Systems, Man, and
Cybernetics (SMC), 2011 IEEE International Conference on. IEEE, 2011.
XXXI. Turki Alharkan, Patrick Martin, IDSaaS: Intrusion Detection System as a
Service in Public Clouds. IEEE/ACM International Symposium on Cluster,
Cloud and Grid Computing, 2012.

XXXII. Waqar, Adeela, et al. “A framework for preservation of cloud users’ data
privacy using dynamic reconstruction of metadata.” Journal of Network and
Computer Applications 36.1, 235-248, 2013.
XXXIII. Y. Wang, J. Wei. Toward protecting control flow confidentiality in cloudbased
computation. Comput. Secur., 52, pp. 106-127, 2015.
XXXIV. Z. Cai, X. Guan, P. Shao, Q. Peng, and G. Sun, “A rough set theory based
method for anomaly intrusion detection in computer network systems,”
Expert Systems, vol. 20, pp. 251-259, 2003.
XXXV. Zhang, Xuyun, et al. “An efficient quasi-identifier index based approach for
privacy preservation over incremental data sets on cloud.” Journal of
Computer and System Sciences 79.5: 542-555, 2013.
XXXVI. Zhao Gansen, Rong Chunming, Li Jin, Zhang Feng, Tang Yong. Trusted
data sharing over untrusted cloud storage providers. In: Proceedings of the
2nd IEEE international conference on cloud computing technology and
science (CloudCom 2010), 2010.
XXXVII. Zissis, Dimitrios, and Dimitrios Lekkas. “Addressing cloud computing
security issues.” Future Generation Computer Systems, 28, 3: 583-592, 2012.

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Abstract:

This paper presents a multi-objective optimization model to optimize the performance of the Combined cooling, heat & power (CCHP) strategy in different climates based on cost, energy consumption and carbon gas production. In order to ensure the reliability of the CCHP's performance strategy and potential load power, potential constraints are added to the contingency model and the impact of increasing the level of reliability on contingency constraints on cost, energy consumption and carbon gas production is analyzed. To develop the proposed multi-objective analysis, a model is proposed to reduce primary energy consumption and carbon emissions, and for different atmospheric conditions, values of energy consumption and carbon gas production are determined. Finally, the proposed problem was applied to the cities of San Francisco, Boston, Miami, Minneapolis and Columbus and coded in the GAMS optimization software environment. Then, based on the numerical results, the capabilities of the proposed scheme in support of optimal CCHP performance planning are evaluated.

Keywords:

Multi Objective Modeling,Power generation,CCHP Heating Unit,Combined Systems,

Refference:

I. W. El-Khattam and M. M. A. Salama, “Distributed generation
technologies, definitions and benefits,” Electr. power Syst. Res., vol. 71,
no. 2, pp. 119–128, 2004.
II. C. Shang, D. Srinivasan, and T. Reindl, “Generation and storage
scheduling of combined heat and power,” Energy, vol. 124, pp. 693–705,
2017.
III. X. Chu, D. Yang, X. Li, and R. Zhou, “Evaluation of CCHP system
performance based on operational cost considering carbon tax,” Energy
Procedia, vol. 142, pp. 2930–2935, 2017.

IV. H. Moradi, I. G. Moghaddam, M. P. Moghaddam, and M. R. Haghifam,
“Opportunities to improve energy efficiency and reduce greenhouse gas
emissions for a cogeneration plant,” in 2010 IEEE International Energy
Conference, pp. 785–790,2010.
V. C.-Z. Li, Y.-M. Shi, S. Liu, Z. Zheng, and Y. Liu, “Uncertain
programming of building cooling heating and power (BCHP) system
based on Monte-Carlo method,” Energy Build., vol. 42, no. 9, pp. 1369–
1375, 2010.
VI. S. Bracco, G. Dentici, and S. Siri, “Economic and environmental
optimization model for the design and the operation of a combined heat
and power distributed generation system in an urban area,” Energy, vol. 55,
pp. 1014–1024, 2013.
VII. A. Smith, R. Luck, and P. J. Mago, “Analysis of a combined cooling,
heating, and power system model under different operating strategies with
input and model data uncertainty,” Energy Build., vol. 42, no. 11, pp.
2231–2240, 2010.
VIII. D. Maraver, A. Sin, J. Royo, and F. Sebastián, “Assessment of CCHP
systems based on biomass combustion for small-scale applications through
a review of the technology and analysis of energy efficiency parameters,”
Appl. Energy, vol. 102, pp. 1303–1313, 2013.
IX. J. Wang, P. Zhao, X. Niu, and Y. Dai, “Parametric analysis of a new
combined cooling, heating and power system with transcritical CO2
driven by solar energy,” Appl. Energy, vol. 94, pp. 58–64, 2012.
X. S. Li, J. Sui, H. Jin, and J. Zheng, “Full chain energy performance for a
combined cooling, heating and power system running with methanol and
solar energy,” Appl. Energy, vol. 112, pp. 673–681, 2013.
XI. E. Cardona, A. Piacentino, and F. Cardona, “Matching economical,
energetic and environmental benefits: an analysis for hybrid CHCP-heat
pump systems,” Energy Convers. Manag., vol. 47, no. 20, pp. 3530–3542,
2006.
XII. A. A. Jalalzadeh-Azar, “A comparison of electrical-and thermal-loadfollowing
CHP systems,” ASHRAE Trans., vol. 110, p. 85, 2004.
XIII. M. Liu, Y. Shi, and F. Fang, “A new operation strategy for CCHP systems
with hybrid chillers,” Appl. Energy, vol. 95, pp. 164–173, 2012.
XIV. A. Rong and R. Lahdelma, “An efficient linear programming model and
optimization algorithm for trigeneration,” Appl. Energy, vol. 82, no. 1, pp.
40–63, 2005.
XV. J.-J. Wang, Y.-Y. Jing, and C.-F. Zhang, “Optimization of capacity and
operation for CCHP system by genetic algorithm,” Appl. Energy, vol. 87,
no. 4, pp. 1325–1335, 2010.
XVI. J. Wang, Z. J. Zhai, Y. Jing, and C. Zhang, “Particle swarm optimization
for redundant building cooling heating and power system,” Appl. Energy,
vol. 87, no. 12, pp. 3668–3679, 2010.
XVII. W. Jiang-Jiang, Z. Chun-Fa, and J. You-Yin, “Multi-criteria analysis of
combined cooling, heating and power systems in different climate zones in
China,” Appl. Energy, vol. 87, no. 4, pp. 1247–1259, 2010.

XVIII. B. Shi, L.-X. Yan, and W. Wu, “Multi-objective optimization for
combined heat and power economic dispatch with power transmission loss
and emission reduction,” Energy, vol. 56, pp. 135–143, 2013.
XIX. E. Thorin, H. Brand, and C. Weber, “Long-term optimization of
cogeneration systems in a competitive market environment,” Appl. Energy,
vol. 81, no. 2, pp. 152–169, 2005.
XX. S. G. Tichi, M. M. Ardehali, and M. E. Nazari, “Examination of energy
price policies in Iran for optimal conFiguration of CHP and CCHP
systems based on particle swarm optimization algorithm,” Energy Policy,
vol. 38, no. 10, pp. 6240–6250, 2010.
XXI. G. Abdollahi and H. Sayyaadi, “Application of the multi-objective
optimization and risk analysis for the sizing of a residential small-scale
CCHP system,” Energy Build., vol. 60, pp. 330–344, 2013.
XXII. K. C. Kavvadias and Z. B. Maroulis, “Multi-objective optimization of a
trigeneration plant,” Energy Policy, vol. 38, no. 2, pp. 945–954, 2010.
XXIII. M. Liu, Y. Shi, and F. Fang, “Optimal power flow and PGU capacity of
CCHP systems using a matrix modeling approach,” Appl. Energy, vol. 102,
pp. 794–802, 2013.
XXIV. L. Wang and C. Singh, “Stochastic combined heat and power dispatch
based on multi-objective particle swarm optimization,” in 2006 IEEE
Power Engineering Society General Meeting, 8, pp 72-87, 2006.
XXV. L. Coles and R. W. Beck, “Distributed generation can provide an
appropriate customer price response to help fix wholesale price volatility,”
in 2001 IEEE Power Engineering Society Winter Meeting. Conference
Proceedings (Cat. No. 01CH37194), vol. 1, pp. 141–143, 2001.
XXVI. J. F. Blinn, “A generalization of algebraic surface drawing,” ACM Trans.
Graph., vol. 1, no. 3, pp. 235–256, 1982.
XXVII. C. Z. Li, Y. M. Shi, and X. H. Huang, “Sensitivity analysis of energy
demands on performance of CCHP system,” Energy Convers. Manag., vol.
49, no. 12, pp. 3491–3497, 2008.
XXVIII. S. Gamou, R. Yokoyama, and K. Ito, “Optimal unit sizing of cogeneration
systems in consideration of uncertain energy demands as continuous
random variables,” Energy Convers. Manag., vol. 43, no. 9–12, pp. 1349–
1361, 2002.

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Abstract:

This paper presents a new approach based upon Perturb and Observe (P&O) to track Maximum Power Point (MPP) in Photovoltaic (PV) systems. This algorithm has a wide step length range which results in a very high response time to changing ambient condition. This method is analogue based and thus no DSP and/or A/D are employed in this system which greatly reduces the complexity and cost. To further increase the total efficiency, a soft switching converter as an interface circuit is applied. Semiconductor devices in underutilized converter entirely are fully soft switched. The proposed system is analyzed and the simulation results are presented. A 135W prototype system is implemented and the stated experimental results confirm the veracity of the theoretical analysis.

Keywords:

Maximum Power Point Tracking,Perturb and Observe,Photovoltaic Panel,Zero Current Switching Converters,

Refference:

I. Adib, E. and H. Farzanehfard. “Family of Zero-Current Transition PWM
Converters.” IEEE Transactions on Industrial Electronics, 55(8): 3055-3063,
2008.
II. Banu, I. V., et al. Comparative analysis of the perturb-and-observe and
incremental conductance MPPT methods, 2017.
III. Brito, M. A. G. d., et al. “Evaluation of the Main MPPT Techniques for
Photovoltaic Applications.” IEEE Transactions on Industrial Electronics, 60(3):
1156-1167, 2013.
IV. Dash, S. K., et al. Comparative analysis of maximum power point (MPP)
tracking techniques for solar PV application using MATLAB simulink, 2016.
V. Hong, Y., et al. Efficient Maximum Power Point Tracking for a Distributed PV
System under Rapidly Changing Environmental Conditions, 2016.
VI. Khatib, T. E., Wilfried Mohamed,Azah “Simplified I-V characteristic tester for
photovoltaic modules using a DC-DC boost converter.” Sustainability
(Switzerland) 9(4): 66-77, 2017.
VII. Mohanty, P., et al. “MATLAB based modeling to study the performance of
different MPPT techniques used for solar PV system under various operating

conditions.” Renewable and Sustainable Energy Reviews 38(Supplement C):
581-593, 2014.
VIII. Murtaza, A. F., et al. “Comparative analysis of maximum power point tracking
techniques for PV applications.” INMIC: 83-88, 2013.
IX. Raj, J. S. C. M. and A. E. Jeyakumar A Novel Maximum Power Point Tracking
Technique for Photovoltaic Module Based on Power Plane Analysis of
Characteristics, 2015.
X. Reza Reisi, A., et al. “Classification and comparison of maximum power point
tracking techniques for photovoltaic system: A review.” Renewable and
Sustainable Energy Reviews, 19(Supplement C): 433-443, 2013.
XI. Rezk, H. and A. M. Eltamaly. “A comprehensive comparison of different
MPPT techniques for photovoltaic systems.” Solar Energy, 112(Supplement
C): 1-11, 2015.
XII. Romero-Cadaval, E., et al. “Grid-Connected Photovoltaic Generation Plants:
Components and Operation.” IEEE Industrial Electronics Magazine, 7(3): 6-20,
2013.
XIII. Sheraz, M. and M. A. Abido. “An efficient MPPT controller using differential
evolution and neural network.” 2012 IEEE International Conference on Power
and Energy (PECon), 6, 378-383, 2012.
XIV. Sreekanth, S. and I. J. Raglend. “A comparitive and analytical study of various
incremental algorithms applied in solar cell.” 2012 International Conference on
Computing, Electronics and Electrical Technologies (ICCEET): 452-456,
2012.

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Abstract:

The pond ash (class F) as an individual material is unsuitable for utilization in pavement constructions due to few undesirable physico-mechanical properties. Treatment of pond ash by suitable additives like cement and lime would improve its usability. The present study is intended to determine the strength and stiffness properties such as Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR) and Resilient modulus (MR) of both untreated and lime-treated pond ash for its pavement subbase application. The experimental investigation illustrates the enhancement in UCS, CBR, and MR properties of lime-treated pond ash compared to untreated/virgin pond ash specimens. Further, a significant improvement was observed at lime content about 8%, which can be considered as optimum addition to pond ash for pavement constructions.

Keywords:

Pond ash,Lime,UCS,CBR,Resilient Modulus,

Refference:

I. American Association of State Highway and Transportation Officials
AASHTO, 1993. AASHTO guide for design of pavement structures,
AASHTO, Washington, D.C.
https://habib00ugm.files.wordpress.com/2010/05/aashto1993.pdf
II. Arora, S., & Aydilek, A. H. (2005). Class F fly-ash-amended soils as
highway base materials. Journal of Materials in Civil Engineering, 17(6),
640-649. https://doi.org/10.1061/(ASCE)0899-1561(2005)17:6(640)
III. CEA “Fly ash generation at coal/lignite based thermal power stations and
its utilization in the country for the year 2016-2017”, New Delhi: Central
Electricity Authority, 2017 http://www.cea.nic.in/tcd.html
IV. Chand, S. K., & Subbarao, C. (2007). Strength and slake durability of lime
treated pond ash. Journal of materials in civil engineering, 19(7), 601-
608. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:7(601)
V. Consoli, N. C., Prietto, P. D. M., Carraro, J. A. H., & Heineck, K. S.
(2001). Behavior of compacted soil-fly ash-carbide lime mixtures. Journal
of Geotechnical and Geoenvironmental Engineering, 127(9), 774-782.
VI. Dev, K. L., & Robinson, R. G. (2015). Pond ash based controlled low
strength flowable fills for geotechnical engineering
applications. International Journal of Geosynthetics and Ground
Engineering, 1(4), 32.https://doi.org/10.1007/s40891-015-0035-1
VII. Ghosh, A. (2009). Compaction characteristics and bearing ratio of pond
ash treated with lime and phosphogypsum. Journal of materials in civil
engineering, 22(4), 343-351. https://doi.org/10.1061/(ASCE)MT.1943-
5533.0000028
VIII. Ghosh, A., & Subbarao, C. (2007). Strength characteristics of class F fly
ash modified with lime and gypsum. Journal of geotechnical and
geoenvironmental engineering, 133(7), 757-766.
https://doi.org/10.1061/(ASCE)1090-0241(2007)133:7(757)
IX. Gupta, D., & Kumar, A. (2017). Performance evaluation of cement-treated
pond ash-rice husk ash-clay mixture as a highway construction
material. Journal of Rock Mechanics and Geotechnical Engineering, 9(1),
159-169. https://doi.org/10.1016/j.jrmge.2016.05.010
https://doi.org/10.1061/(ASCE)1090-0241(2001)127:9(774)
X. J. Groeger, G. Rada, and A. Lopez, “AASHTO T307 — Background and
Discussion,” in Resilient Modulus Testing for Pavement Components, ed.
G. Durham, W. DeGroff, and W. Marr (West Conshohocken, PA: ASTM
International, 2003), 16 29. https://doi.org/10.1520/STP12519S
XI. Kang, X., Ge, L., Kang, G. C., & Mathews, C. (2015). Laboratory
investigation of the strength, stiffness, and thermal conductivity of fly ash
and lime kiln dust stabilised clay subgrade materials. Road Materials and
Pavement Design, 16(4), 928-945.
https://doi.org/10.1080/14680629.2015.1028970

XII. Kumar Bera, A., Ghosh, A., & Ghosh, A. (2007). Compaction
characteristics of pond ash. Journal of materials in civil
engineering, 19(4), 349-357. https://doi.org/10.1061/(ASCE)0899-
1561(2007)19:4(349)
XIII. Lav, A. H., Lav, M. A., & Goktepe, A. B. (2006). Analysis and design of a
treated fly ash as pavement base material. Fuel, 85(16), 2359-2370.
https://doi.org/10.1016/j.fuel.2006.05.017
XIV. National Cooperative Highway Research Program (NCHRP). (2004).
Guide for mechanistic-empirical design of new and rehabilitated pavement
structures. National Cooperative Highway Research Program 1-37 A.
http://onlinepubs.trb.org/onlinepubs/archive/mepdg/2appendices_RR.pdf
XV. Nicholson, P., and Kashyap, V. (1993). “Fly ash stabilization of tropical
Hawaiian soils.” Geotechnical Special Publication, No. 36, ASCE, New
York, 15–29. http://worldcat.org/isbn/0872629864
XVI. Pani, A., & Singh, S. P. (2017). Influences of curing conditions on
strength and microstructure of lime-amended fly ash.
http://hdl.handle.net/2080/2749
XVII. Patel, S., & Shahu, J. T. (2016). Resilient response and permanent strain
of steel slag-fly ash-dolime mix. Journal of Materials in Civil
Engineering, 28(10), 04016106. https://doi.org/10.1061/(ASCE)MT.1943-
5533.0001619
XVIII. Patel, S., & Shahu, J. T. (2018). Comparison of Industrial Waste Mixtures
for Use in Subbase Course of Flexible Pavements. Journal of Materials in
Civil Engineering, 30(7), 04018124.
https://doi.org/10.1061/(ASCE)MT.1943-5533.0002320
XIX. Puppala, A. J., Ramakrishna, A. M., & Hoyos, L. R. (2003). Resilient
moduli of treated clays from repeated load triaxial test. Transportation
research record, 1821(1), 68-74. https://doi.org/10.3141/1821-08
XX. Rout, R. K., Ruttanapormakul, P., Valluru, S., & Puppala, A. J. (2012).
Resilient moduli behavior of lime-cement treated subgrade soils. In Geo
Congress, ASCE (pp. 1428-1437). https://doi.org/10.1061/9780784412121
XXI. Saghafi, B., Nageim, H. A., VisuliosMPhil, P., & Ghazireh, N. (2012).
Use of waste limestone dust and steel slag in UK highways type 1
unbound mixtures. Proceedings of the Institution of Civil Engineers-
Construction Materials, 166(2), 99-107.
https://doi.org/10.1680/coma.11.00029
XXII. Samanta, M. (2018). Investigation on geomechanical behaviour and
microstructure of cement-treated fly ash. International Journal of
Geotechnical Engineering, 12(5), 449-
461.https://doi.org/10.1080/19386362.2017.1295623
XXIII. Sarkar, R., & Dawson, A. R. (2017). Economic assessment of use of pond
ash in pavements. International Journal of Pavement Engineering, 18(7),
578-594. https://doi.org/10.1080/10298436.2015.1095915

XXIV. Singh, S. P., & Ramaswamy, S. V. (2006). Utilisation Potential of Cement
Stabilised Flyash-GBFS Mixes in Highway Construction. Water and
Energy Abstracts, 16(1).
http://www.indianjournals.com/ijor.aspx?target=ijor:wea&volume=16&is
sue=1&article=143
XXV. Sivapullaiah, P. V., Prashanth, J. P., & Sridharan, A. (2000). Optimum
lime content for fly ashes and the role of the curing period. Journal of
testing and evaluation, 28(6), 499-506. https://doi.org/10.1520/JTE12141J
XXVI. Sivapullaiah, P., & Moghal, A. (2011). CBR and strength behavior of
class F fly ashes treated with lime and gypsum. International Journal of
Geotechnical Engineering, 5(2), 121-130.
https://doi.org/10.3328/IJGE.2011.05.02.121-130
XXVII. Suthar, M., & Aggarwal, P. (2018). Bearing ratio and leachate analysis of
pond ash treated with lime and lime sludge. Journal of Rock Mechanics
and Geotechnical Engineering, 10(4), 769-
777.https://doi.org/10.1016/j.jrmge.2017.12.008
XXVIII. Titli, H., Coenen, A., & Elias, M. (2012). Resilient Characteristics of
Bottom Ash and Bottom Ash-Soil Mixtures. In Testing and Specification
of Recycled Materials for Sustainable Geotechnical Construction. ASTM
International.https://doi.org/10.1520/JAI103700
XXIX. Viswanathan, R., Saylak, D., Estakhri, C. K., Tauferner, D &
Chimakurthy, H. (1996). Evaluation of the Use of Coal Combustion By-
Products in Highway and Airfield Pavement Construction (No. TX-
97/2969-1F). https://trid.trb.org/view/572404
XXX. Wong, C., & Ho, M. K. (1989). Experimental Fly Ash Base. Farm-To-
Market Road 1093, Fulshear, TEXAS (No. DHT-
17).https://trid.trb.org/view/317525

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Abstract:

As indicated by Breast Cancer Research, Breast malignancy is the disease most unmistakable in the female populace of the world. According to the clinical specialists, identifying this malignant growth in its beginning time helps in sparing lives. The site cancer.net offers individualized aides for more than 120 sorts of malignancy and related innate disorders. For visualization of bosom malignant growth through innovation, AI strategies are, for the most part, favored. In this structure, an adaptable group AI calculation by surveying among different strategies is proposed for the conclusion of bosom disease. Reports utilizing the Wisconsin Breast Cancer database is utilized. The point of this system is to analyze and clarify how ANN and calculated relapse calculation together gives a superior answer to identify Breast malignancy even though the factors are diminished. This procedure demonstrates that the neural system is additionally compelling for necessary human information. We can do pre-finding with no uncommon therapeutic learning.

Keywords:

Artifical Neural Network,Convolutional Networks,Machine Learning,Support Vector Machine,

Refference:

I. AlirezaOsarech, BitaShadgar,”A Computer Aided Diagnosis System for Breast
Cancer”,International Journal of Computer Science Issues, Vol. 8, Issue 2, March
2011.
II. B. Krawczyk, M. Galar, L.Jelen, F.Herrera (2016), “Evolutionary undersampling
boosting for imbalanced classification of breast cancer malignancy”, Article in
Applied soft computing, Elsevier B.V., pp 1-14.
III. Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm
by M.R.Al- Hadidi, A. Alarabeyyat and M. Alhanahnah.
IV. Breast cancer mass localization based on machine learning by A. Qasem et al
V. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method.
Author : Tongan Cai, Hongliang He , Wenyu Zhang
VI. Biopsy-guided learning with deep convolutional neural networks for Prostate
Cancer detection on multiparametric MRI by Yohannes Tsehaya, Nathan Laya,
Xiaosong Wanga, Jin Tae Kwaka, Baris Turkbeyb, Peter Choykeb, Peter Pintob,
Brad Woodc, and Ronald M. Summersa.
VII. Cao, D.S., Xu, Q. S., Liang, Y.Z., Zhang, L.X., Li, H.-D. (2010), “The boosting:
A new idea of building models”, Chemometrics and Intelligent Laboratory
Systems, Vol.4, pp 1-11.
VIII. Comparative Study of Machine Learning Algorithms for Breast Cancer Detection
and Diagnosis by Dana Bazazeh and Raed Shubair

IX. Dongdong Sun , Minghui Wang and Huanqing Feng, “Prognosis prediction of
human breast cancer by integrating deep neural network and support vector
machine: Supervised feature extraction and classification for breast cancer
prognosis prediction”, 978-1-5386-1937- 7/17/$31.00 ©2017 IEEE.
X. Ebrahim Edriss Ebrahim Ali1 , Wu Zhi Feng2- “Breast Cancer Classification
using Support Vector Machine and Neural Network”– International Journal of
Scienceand Research(IJSR) Volume 5 Issue 3, March 2016.
XI. García, F. Herrera (2009), “Evolutionary undersampling for classification with
imbalanced datasets: proposals and taxonomy”, Evol. Computing, Vol.17, pp
275–306.
XII. Haifeng Wang and Sang Won Yoon – Breast Cancer Prediction using Data
Mining Method, IEEE Conference paper.
XIII. Ismail Saritas, “Prediction of Breast Cancer Using Artificial Neural Networks”,
Springer Science+Business Media, LLC 2011.
XIV. J. Huang, C.X. Ling (2005), “Using AUC and accuracy in evaluating learning
algorithms”, IEEE Trans. Knowl. Data Eng., Vol. 17(3), pp 299–310.
XV. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera (2012), “A
review on ensembles for the class imbalance problem: bagging-, boosting-, and
hybrid based approaches”, IEEE Trans. Systems Man Cybern. C: Appl. Rev., Vol.
42 (4), pp 463–484.
XVI. Rejani YIA, Selvi ST (2009) Early detection of breast cancer using SVM
classifier technique. International Journal on Computer Science and Engineering
1(3):127-130.
XVII. R.W. Scarff, H. Torloni (1968), “Histological typing of breast tumors”,
International histological classification of tumours, World Health Organization,
Vol. 2 (2), pp 13–20.

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Abstract:

Routing is the most widely seen in many wireless sensor network (WSN) applications. Some networks work routinely on single path and this is limited. Many issues are identified in routing in WSN such as traffic, malicious nodes, etc. In this paper, the new enhanced threshold-based policy (BP-T) and a heuristic policy, which seriously controls traffic bifurcations at overlay focused. This will reduce the traffic and find the optimal with multiple routing can be identified. Results show the performance of the proposed system.

Keywords:

Wireless sensor network,routing,BPT,OBP,

Refference:

I. D. Xu, et.al, “Link-state routing with hop-by-hop forwarding can achieve
optimal traffic engineering” IEEE/ACM Trans. Netw., vol. 19, no. 6, pp.
1717–1730, Dec. 2011.
II. J. Han, et.al, “Topology aware overlay networks,” in Proc. IEEE INFOCOM,
Mar. 2005, pp. 25542565.
III. K. Kondaveti, T. K. Latha and Md. Amanatulla, “Data Integrity and Delay
Differentiated Services with Dynamic Routing in Wireless Sensor Networks”,
IJDCST @ Jan,-2017,Issue-V-5,I-1,SW-12.
IV. L. Bui, et.al, “Novel architectures and algorithms for delay reduction in backpressure
scheduling and routing”, in Proc. IEEE INFOCOM, Apr. 2009, pp.
2936–2940.
V. L. L. Peterson et.al “Computer Networks: A Systems Approach”, 4th ed. San
Francisco, CA, USA: Morgan Kaufmann, 2007.
VI. M. J. Neely, et.al, “Dynamic power allocation and routing for time-varying
wireless networks” IEEE J. Sel. Areas Commun., vol. 23, no. 1, pp. 89–103,
Jan. 2005.
VII. Rai, C.-P, et.al “Loop-free backpressure routing using link-reversal
algorithms”, IEEE/ACM Trans. Net., vol. 25, no. 5, pp. 2988–3002, Oct.
2017.
VIII. S. A. Khan et.al, “Performance Analysis of a ZigBee Beacon Enable Cluster
Tree Network”, Proc. Int l Conf. Electrical Eng. (ICEE), Apr.
IX. W. Khan, L, et.al, “Autonomous routing algorithms for networks with widespread
failures” in Proc. IEEE MILCOM, Oct. 2009, pp. 1–6.
X. Yu-Kai Huang, et.al, “Distributed Throughput Optimization for ZigBee
Cluster-Tree Network”, IEEE Trans. Paral. Distr. Syst., vol. 23, no. 3, pp.
513-520, Mar. 2012.

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Abstract:

This article outlines the theoretical, methodological and practical problems of analysis and synthesis of complex systems. The static or dynamic property of a system can be evaluated when the system structure is known, and all its parameters are specified. The objective of system analysis is to find an exact analytical or approximate solution of equations based on a corresponding mathematical model, as well as its further research. Theoretical conclusions and results have allowed to build mathematical models applicable to the management of objects with different principles of operation, in particular, to the management of complex technical and technological objects that can be represented as nonlinear dynamic systems. Nonlinear dynamic integral models are deemed appropriate to study and design such systems in some critical application.

Keywords:

technical systems,engineering systems,mathematical models,management,system analysis,complex system,

Refference:

I. A.A. Denisov, Modern problems of system analysis: Information bases
(Publishing house of St. Petersburg State University, St. Petersburg, 2005).
II. A.A. Kolesnikov, Synergetic methods of control of complex systems: theory
of system synthesis (KomKniga, Moscow, 2006)
III. A.V. Panteleev, A.S. Bartacovsky, E.A. Rudenko, Nonlinear control
systems: description, analysis and synthesis (High school book, Moscow,
2008)
IV. I.Yu. Tyukin, V.A. Terekhov, Adaptation in nonlinear dynamical
systems(LKI, Moscow, 2008)
V. V.G. Fetisov, I.V. Fetisov, I.I. Paninа, Some open questions of synthesis of
systems containing Hλ-operators (Scientific and technical Bulletin of the
Volga region, Kazan, 2013. – № 6. – P. 469–473)
VI. V.G. Fetisov, V.I. Filippenko, O.I. Okhrimenko, Qualitative and
quantitative methods of system analysis (FGBOU HPE «JURUES»,
Shakhty, 2011).
VII. V.G. Fetisov, I.I. Paninа,Classification of the main types of systems based
on their degree of complexity (Actual problems of technique and
technology, Shakhty, 2013).
VIII. V.G. Fetisov, O.I. Okhrimenko, I.I. Paninа,Selected chapters of modern
control theory with applications (ISOiP (branch) of the DSTU, Shakhty,
2015).

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Abstract:

In present days, low power Very Large Scale Integration (VLSI) circuit assumes a significant job in structuring effective vitality sparing electronic frameworks for rapid execution. In this, low power utilization one of the most significant criteria in different gadgets like cell phones, workstations, High-speed work stations, and so on. FinFETs area unit multi-door transistors that supply higher entry direct management is very little component advancements. They show higher and lower spillage contrasted with the Complementary metal oxide semiconductor planar. As appeared in Figure one, the door is created of a slim balance that associates the availability of the channel on to form the avenue. The avenue is middle between 2 facet entryways on 2 inverse sides. the weather of the door area unit calculable through the entry length, chemical compound thickness, balance dimension, and balance tallness. The activity of the FinFET semiconductor unit is basically similar because of the CMOS planate. In this paper, an SR Latch utilizing eight transistors has been proposed. The proposed SR Latch is planned to utilize the CADENCE EDA apparatus and re-enacted utilizing the Specter Virtuoso at FinFET 18 nm innovation. The proposed outcomes as far as power, area, and delay from table3, table4, table5, and table6.

Keywords:

Low power,delay,area,FinFET SR Latch,FinFET NAND gate,DSP,VLSI,

Refference:

I. Alshraiedeh and A. Kodi An adaptive routing algorithm to improve
lifetime reliability in NoCs architecture, IEEE International Symposium
on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
(DFT) (2016), pp. 127–130.
II. Amulya Boyina, K. Praveen Kumar “Active Coplanar Wave guide Fed
Switchable Multimode Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-
14, No.-4, July-August (2019) pp 188-196.
III. Azizi, M. M. Khellah, V. K. De & F. N. Najm, Variations aware lowpower
design and block clustering with voltage scaling. IEEE Trans. Very
Large Scale Integr. Syst. 15, 746 (2007).
IV. Bahadori, M. Kamal, A. Afzali-Kusha & M. Pedram, A comparative study
on performance and reliability of 32-bit binary adders. Integr. VLSI J. 53,
54 (2016).
V. C. Catanzaro, K. Asanovic, D. A. Patterson, and K. A. Yelick, The
landscape of parallel computing research: A view from berkeley. EECS
Dep. Univ. Calif. Berkeley Tech. Rep. UCBEECS2006183 18, 19 (2006).

VI. Chen, A. Shafaei, Y. Wang, S. Chen, and M. Pedram, Maximizing the
performance of NoC-based MPSoCs under total power and power density
constraints, 17th International Symposium on Quality Electronic Design
(ISQED) (2016), pp. 49–56.
VII. Dousti, Q. Xie, X. Lin, Y. Wang, A. Shafaei, M. Ghasemi-Gol, and M.
Pedram, 5 nm FinFET standard cell library optimization and circuit
synthesis in near-and super-threshold voltage regimes, 2014 IEEE
Computer Society Annual Symposium on VLSI (2014), pp. 424–429.
VIII. G. Friedman, K. Xu, R. Patel and P. Raghavan, Exploratory design of onchip
power delivery for 14, 10, and 7 nm and beyond FinFET ICs.
Integration 61, 11 (2018).
IX. Hwang, Y. Li, S. Member, and T. Li, Process-variation effect, metal-gate
work-function fluctuation, and random-dopant fluctuation in emerging
CMOS technologies. IEEE Trans. Electron Devices 57, 437 (2010).
X. J. R. Ferreira, Analysis of many-core CPUs simulators. Tech. Rep.,
Instituto Superior Tecnico, Universidade de Lisboa, pp. 1–10.
XI. K Satish Reddy a, K Praveen Kumar, Habibulla Khan, Harswaroop Vaish
“Measuring the surface properties of a Novel 3-D Artificial Magnetic
Material” 2nd International Conference on Nanomaterials and
Technologies (CNT 2014), Elsevier Procedia material Science.
XII. K.Praveen Kumar, “Design of 3D EBG for L band Applications” IEEE
International conference on communication technology ICCT-April-2015.
Noor Ul Islam University Tamilnadu.
XIII. K.Praveen Kumar, “Effect of 2DEBG structure on Monopole Antenna
Radiation and Analysis of It’s characteristics” IEEE International
conference on communication technology ICCT-April-2015. Noor Ul
Islam University Tamilnadu.
XIV. K.Praveen Kumar, “Mutual Coupling Reduction between antenna
elements using 3DEBG” IEEE International conference on
communication technology ICCT-April-2015. Noor Ul Islam University
Tamilnadu.
XV. K.Praveen Kumar, “The surface properties of TMMD-HIS material; A
measurement” IEEE International conference on communication
technology ICCT-April-2015. Noor Ul Islam University Tamilnadu.
XVI. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XVII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.

XVIII. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XIX. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XX. K.Praveen Kumar, Dr Habibulla Khan ” The surface properties of
TMMD-HIS material; a measurement” IEEE International conferance on
electrical, electronics, signals, communication & optimization EESCO –
January 2015.
XXI. K.Praveen Kumar, Dr. Habibulla Khan ” Active progressive stacked
electromagnetic band gap structure (APSEBG) structure design for low
profile steerable antenna applications” International Conference on
Contemporary engineering and technology 2018 (ICCET-2018) March 10
– 11, 2018. Prince Shri Venkateshwara Padmavathy Engineering College,
Chennai.
XXII. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced
research in dynamical and control systems, Vol-10, Special issue-03,
2018.
XXIII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XXIV. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structures
for Mutual coupling reduction in antenna arrays; A comparative study”
International Conference on Contemporary engineering and technology
2018 (ICCET-2018) March 10 – 11, 2018. Prince Shri Venkateshwara
Padmavathy Engineering College, Chennai.
XXV. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization of
Optimized stacked electromagnetic band gap ground plane for low profile
patch antennas” International journal of pure and applied mathematics,
Vol 118, No. 20, 2018, 4765-4776.
XXVI. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna Design
for C-Band Applications”, International Journal of Innovative Technology
and Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XXVII. Kumaraswami Gajula, Amulya Boyina, K. Praveen Kumar “Active Quad
band Antenna Design for Wireless Medical and Satellite Communication
Applications” Journal Of Mechanics Of Continua And Mathematical
Sciences (JMCMS), Vol.-14, No.-4, July-August (2019) pp 239-252.

XXVIII. Lim, H. Kim, H. Oh, and S. Kang, Dynamic voltage frequency scalingaware
refresh management for 3D DRAM over processor architecture.
Electron. Lett. 53, 910 (2017).
XXIX. Liu, R. Kim, P. Wettin, R. Marculescu, D. Marculescu, and P. P. Pande,
Energy-efficient VFI-partitioned multicore design using wireless NoC
architectures, Proceedings of the 2014 International Conference on
Compilers, Architecture and Synthesis for Embedded Systems-CASES
’14 (2014), pp. 1–9.
XXX. M. E. Salehi and H. Nejatollahi Voltage scaling & dark silicon in
symmetric multicore processors. J. Supercomput. 71, 3958 (2015).
XXXI. N. S. Kim, S. K. Khatamifard, M. Resch & U. R. Karpuzcu, VARIUS-TC:
A modular architecture-level model of parametric variation for thinchannel
switches. Proc. 34th IEEE Int. Conf. Comput. Des. ICCD 2016
(2016), pp.654–661.
XXXII. Navi, A. Arasteh, M. Hossein Moaiyeri, M. Taheri, and N. Bagherzadeh,
An energy and area efficient 4:2 compressor based on FinFETs. Integr.
VLSI J. 60, 224 (2018).
XXXIII. Nejatollahi, M. Ersali, and S. Nasab, Reliability-aware voltage scaling of
multicore processors in dark silicon era. Big Data and HPC: Advances in
Parallel Computing Ecosystem and Convergence (2018), Vol. 33, pp.242–
262.
XXXIV. S. S. Majzoub, R. A. Saleh, S. J. E. Wilton & R. K. Ward, Energy
optimization for many-core platforms: Communication and PVT aware
voltage-island formation and voltage selection algorithm. IEEE Trans.
Comput. Des. Integr. Circuits Syst. 29, 816 (2010).
XXXV. Singh, B. S. Reniwal, V. Vijayvargiya, V. Sharma & S. K. Vishvakarma,
Ultra low power-high stability, positive feedback controlled (PFC) 10T
SRAM cell for look up table (LUT) design. Integration 62, 1 (2018).
XXXVI. Stamelakos, S. Xydis, G. Palermo, and C. Silvano, Workload- and
process-variation aware voltage/frequency tuning for energy efficient
performance sustainability of NTC manycores. Integration (2018), In
Press.
XXXVII. Stillmaker and B. Baas, Scaling equations for the accurate prediction of
CMOS device performance from 180 nm to 7 nm. Integration 58, 74
(2017).
XXXVIII. T. Cui, J. Li, Y. Wang, S. Nazarian & M. Pedram, “An exploration of
applying gate-length-biasing techniques to deeply-scaled FinFETs
operating in multiple voltage regimes”, IEEE Transi. Emer. Top. Compu.
six, 172 (2018).
XXXIX. Uddin, R. Poss, and C. Jesshope, Cache-based high-level simulation of
microthreaded many-core architectures. J. Syst. Archit. 60, 529 (2014).

XL. Venkateswar Rao, Praveen Kumar Kancherla, Sunita Panda “Multiband
slotted Elliptical printed Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-14,
No.-4, July-August (2019) pp 378-386.
XLI. W. Jose, A. R. Silva, H. Neto, & M. Vestias, Modeling and simulation of
a many-core architecture using systemC. Conf. Electron. Telecommun.
Comput. 17, 146 (2014).
XLII. X. Lin, Y. Wang, & M. Pedram, Joint sizing and adaptive independent
gate control for FinFET circuits operating in multiple voltage regimes
using the logical effort method, 2013 IEEE/ACM International
Conference on Computer-Aided Design (ICCAD), (2013), pp. 444–449.
XLIII. Z. Hao, S. X.-D. Tan, and G. Shi, Statistical full-chip total power
estimation considering spatially correlated process variations. Integr.
VLSI J. 46, 80 (2013).

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Abstract:

The main intention of this paper was to deliver some of the distinctive features of the Vlasov equation and Landau damping from the perspective of mathematical physics. The main thrust can be reviewed as; Vlasov equation is understood from the origin point of view. The mean field is limit of the classical Nbody problem, is depicted in pure mathematical and also statistical guidelines. We also axiomatically concluded that the Vlasov equation is completely justified as one major source that led to numeral of open problems in mathematical physics: either from molecular chaos to the problems of kinetic theory. We have delivered the mathematics of the Vlasov equations: particularly the traditional partial differential equation analysis in the functional spaces. The problem is observed to be converted as a general transport equation and relaying on the well-posedness of the equation and preserving the transport structure the Vlasov equation is solved for pivotal analytic solutions and compared with the computational solution obtained using solver codes. The analysis of the Vlasov-Poisson equation and its qualitative properties and are focused on the mathematical aspects. In this paper Landau damping is identified numerically for 1D model of non relativistic Vlasov equation.

Keywords:

Vlasov equation,Landau dampin,functional spaces,Semi Lagrangian Schemes,

Refference:

I. Biskamp. D, “Magnetohydrodynamic Turbulence”,
doi:10.1017/cbo9780511535222, 2003.
II. Blaisdell, G. A,“Review of “Implicit Large Eddy Simulation: Computing
Turbulent Fluid Dynamics””, AIAA Journal, 46(12), 3168-3170.
doi:10.2514/1.40931, 2008.
III. Braun. W.&Hepp. K,“TheVlasov dynamics and its fluctuations in the 1/N
limit of interacting classical particles”, Communications in Mathematical
Physics, 56(2), 101-113. doi:10.1007/bf01611497, 1977.
IV. Dafermos. C. M & Feireisl. E, Handbook of differential equations:
evolutionary equations Boston: Elsevier, 2004.
V. Degond. P,“Spectral theory of the linearized Vlasov-Poisson
equation”, Transactions of the American Mathematical Society, 294(2), 435-
435. doi:10.1090/s0002-9947-1986-0825714-8, 1986.
VI. Derfler. H & Simonen, T. C,“Landau Waves: An Experimental
Fact”, Physical Review Letters, 17(4), 172-175.
doi:10.1103/physrevlett.17.172, 1966.
VII. Elskens. Y, “Irreversible behaviors in Vlasov equation and many-body
Hamiltonian dynamics: Landau damping, chaos and granularity”, Topics in
Kinetic Theory, 89-108. doi:10.1090/fic/046/03, 2005.

VIII. Glassey. R & Schaeffer. J,“On time decay rates in landau
damping”, Communications in Partial Differential Equations, 20(3-4), 647-
676. doi:10.1080/03605309508821107, 1995.
IX. Glassey. R & Schaeffer. J, “Time decay for solutions to the linearized Vlasov
equation”, TransportTheory and Statistical Physics, 23(4), 411-453.
doi:10.1080/00411459408203873, 1994.
X. Horst. E,“On the asymptotic growth of the solutions of the vlasov-poisson
system”, Mathematical Methods in the Applied Sciences, 16(2), 75-85.
doi:10.1002/mma.1670160202, 1993.
XI. Horst. E. & Neunzert. H, “On the classical solutions of the initial value
problem for the unmodified non-linear Vlasov equation I general theory”,
Mathematical Methods in the Applied Sciences, 3(1), 229-248.
doi:10.1002/mma.1670030117, 1981.
XII. Ng. K. Y & Landau damping. doi:10.2172/1002000, 2010.
XIII. Pfaffelmoser. K,“Global classical solutions of the Vlasov-Poisson system in
three dimensions for general initial data”, Journal of Differential
Equations, 95(2), 281-303. doi:10.1016/0022-0396(92)90033-j, 1992.
XIV. Robert. R, Statistical Mechanics and Hydrodynamical
Turbulence. Proceedings of the International Congress of
Mathematicians, 1523-1531. doi:10.1007/978-3-0348-9078-6_85, 1995.
XV. Ryutov. D. D,“Landau damping: half a century with the great discovery”,
Plasma Physics and Controlled Fusion, 41(3A). doi:10.1088/0741-
3335/41/3a/001, 1999.
XVI. Vlasov. A. A,“The Vibrational Properties of an Electron Gas”, Soviet Physics
Uspekhi, 10(6), 721-733. doi:10.1070/pu1968v010n06abeh003709, 1968.

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Abstract:

Machines with intermittent movement of working body are widely used in industry. Currently, mechanisms with unilateral constraints or variable structures are used as actuators of such machines. Output link stops in most mechanisms are provided by periodic rupture of the kinematic connection between the links. This disadvantage limits the use of these mechanisms in high speed machines, since impacts occur at the end or beginning of the motion phase. So, there is a relevant task to create, analyze and effectively put into practice intermittent motion mechanisms (IMMs) in which during operation the kinematic constraint between the links is not broken. The author proposes and analyzes planetary trains, which include modified elliptical wheels. The variable transfer function of a non-circular wheel pair and certain dimensions of the mechanism links make it possible to obtain required motion function. The kinematics of the proposed IMMs is analyzed, as a result of which the functions of the angle of rotation and the analogue of the output shaft angular velocity are found and constructed. Created mechanisms due to the use of gears perform reliability and compactness with the possibility of transferring great forces, and they can find application in metalworking machinery, automatic lines, robotics, transporters.

Keywords:

Rotational motion,Intermittent motion,Elliptical gearwheels,Planetary mechanism,Kinematic analysis,Angular velocity,

Refference:

I. AnI. K., “Synthesis, geometric and strength calculations of rotor
hydromachines planetary mechanisms with non-circular gears”, Doctor
thesis, Tomsk Polytechnic University, Tomsk, 2001
II. ArtobolevskiI. I., “Mechanisms in modern engineering design”, MIR
Press, Moscow, USSR, 1986.
III. BickfordJ. H., “Mechanisms for intermittent motion”, Industrial Press
Inc., New York, USA, 1972.
IV. ChangZ., XuC., PanT., WangL., ZhangX., “A general framework for
geometry design of indexing cam mechanism”, Mechanismand Machine
Theory, Volume: 44, Pages: 2079 – 2084, 2009.
https://doi.org/10.1016/j.mechmachtheory.2009.05.010
V. CoxeterH.S.M., “Introduction to Geometry”, Wiley Publishing House.
New York, USA, 1969.
VI. Dooner D. B., Palermo A., Mundo D., “An intermittent motion
mechanism incorporating a Geneva wheel and a gear train”, Transactions
of the Canadian Society for Mechanical Engineering, Volume: 38(3),
Pages: 359 – 372. 2014, https://doi.org/10.1139/tcsme-2014-0026

VII. DoonerD. B., “Use of noncircular gears to reduce torque and speed
fluctuations in rotating shafts”, Journal of Mechanical Design, Volume:
119(2), Pages: 299 – 306. 1997. https://doi.org/10.1115/1.2826251
VIII. FiglioliniG., AngelesJ., “Synthesis of conjugate Geneva mechanisms
with curved slots”, Mechanismand Machine Theory, Volume: 37, Pages:
1043 – 1061, 2002. https://doi.org/10.1016/S0094-114X(02)00062-9
IX. FiglioliniG., ReaP., AngelesJ., “The pure-rolling cam-equivalent of the
Geneva mechanism”, Mechanismand Machine Theory, Volume: 41,
Pages: 1320 – 1335, 2006.
https://doi.org/10.1016/j.mechmachtheory.2006.01.002
X. FreudensteinF., Chen C. K., “Variable-ratio chain drives with noncircular
sprockets and minimum slack-theory and application”, Journal of
Mechanical Design, Volume: 113(3), Pages: 253 – 262, 1991.
https://doi.org/10.1115/1.2912777
XI. HanJ. Y., “Complete balancing of the shaking moment in spatial linkages
by adding planar noncircular gears”,Archive of Applied Mechanics.
Volume: 67(1-2), Pages: 44 – 49. 1997.
https://doi.org/10.1007/BF00787138
XII. KozhevnikovS. N., EsipenkoY. I., RaskinY. M., “Mechanisms”,
Mechanical Engineering Press, Moscow, USSR, 1976.
XIII. LeeJ. J., JanB. H., “Design of Geneva mechanisms with curved slots for
non-undercutting manufacturing”, Mechanismand Machine Theory,
Volume: 44, Pages: 1192 – 1200, 2009.
https://doi.org/10.1016/j.mechmachtheory.2008.09.003
XIV. LevitskiyN.I., “Theory of mechanisms and machines”, Nauka Press.
Moscow, USSR, 1979
XV. LinC., XiaX., LiP., “Geometric design and kinematics analysis of
coplanar double internal meshing non-circular planetary gear train”,
Advances in Mechanical Engineering. Volume: 10(12), Pages 27 – 35.
2018, https://doi.org/10.1177/1687814018818910
XVI. LitvinF.L., FuentesA., “Gear geometry and applied theory”,Cambridge
University Press: Cambridge, UK, 2004.
XVII. LitvinF. L., Gonzalez-PerezI., FuentesA., HayasakaK., “Design and
investigation of gear drives with non-circular gears applied for speed
variation and generation of functions”, Computer Methods in Applied
Mechanics and Engineering. Volume: 197, Pages: 3783 – 3802. 2008.
https://doi.org/10.1016/j.cma.2008.03.001
XVIII. LiuD., BaY., RenT., “Flow fluctuation abatement of high-order elliptical
gear pump by external noncircular gear drive”, Mechanismand Machine
Theory. Volume: 134, Pages: 338 – 348. 2019.
https://doi.org/10.1016/j.mechmachtheory.2019.01.011

XIX. MundoD., “Geometric design of a planetary gear train with non-circular
gears,” Mechanismand Machine Theory, Volume: 41, Pages: 456 – 472.
2006. https://doi.org/10.1016/j.mechmachtheory.2005.06.003
XX. PrikhodkoA.A., SmelyaginA.I., “Dynamic analysis of rotationally
reciprocating stirred tank with multiple impellers”, Proceedings of 2015
International Conference on Mechanical Engineering, Automation and
Control Systems, Pages: 1 − 5. 2015.
https://doi.org/10.1109/MEACS.2015.7414910
XXI. PrikhodkoA.A., SmelyaginA.I., “Dynamics of rotationally reciprocating
stirred tank with planetary actuator”, Journal of Physics:Conference
Series. Volume: 858, 012026. 2017.https://doi.org/10.1088/1742-
6596/858/1/012026
XXII. Prikhod’koA.A., SmelyaginA.I., “Investigation of power consumption in
a mixing device with swinging movement of the actuating
element”,Chemical and Petroleum Engineering. Volume: 54, Pages: 150
– 155. 2018.https://doi.org/10.1007/s10556-018-0454-7
XXIII. PrikhodkoA.A., SmelyaginA.I., “Kinematic analysis of mechanism for
converting rotational motion into reciprocating rotational motion”,
Procedia Engineering.Volume: 129, Pages: 87–92. 2015.
https://doi.org/10.1016/j.proeng.2015.12.013
XXIV. PrikhodkoA.A., SmelyaginA.I., TsybinA.D., “Kinematics of planetary
mechanisms with intermittent motion”, Procedia Engineering, Volume:
206, Pages: 380–385. https://doi.org/10.1016/j.proeng.2017.10.489
XXV. SclaterN., ChironisN. P., “Mechanisms and mechanical devices
sourcebook”, McGraw-Hill Press. New York, USA, 2001.
XXVI. SmelyaginA.I., “Structure of mechanisms and machines”, Novosibirsk
State Technical University Press. Novosibirsk, Russia, 2001.
XXVII. SmelyaginA.I., ChoosovitynN. A., KonstantinovaJ.V., Sannikova J. V.,
“The structural and parametrical synthesis of machines and mechanisms”,
Proceedings of The Fifth Russian-Korean International Symposium on
Science and Technology, Pages: 257 – 261.
2001.https://doi.org/10.1109/KORUS.2001.975117.
XXVIII. SmelyaginA.I., Prikhod’koA.A., “Structure and kinematics of a planetary
converter of the rotational motion into the reciprocating rotary motion”,
Journal of Machinery Manufacture and Reliability. Volume: 45(6),
Pages: 500 – 505. 2016. https://doi.org/10.3103/S1052618816060108
XXIX. SujanV. A., MeggiolaroM.A., “Dynamic optimization of Geneva
mechanisms”, Proceedings of the International Conference on Gearing,
Transmissions and Mechanical Systems, Pages: 687 – 696, 2000.

XXX. TeradaH., ZhuY., SuzukiM., ChengC., TakahashiR., “Developments of a
knee motion assist mechanism for wearable robot with a non-circular
gear and grooved cams”, Mechanismsand MachineScience. Volume: 3,
Pages: 69 – 76. 2012, https://doi.org/10.1007/978-94-007-2727-4_6
XXXI. WaldronK. J., KinzelG. L., “Kinematics, Dynamics, and Design of
Machinery”, John Wiley & Sons Press Inc, New York, USA, 1999.
XXXII. ZhengF., HuaL., HanX., ChenD., “Generation of noncircular bevel gears
with free-form tooth profile and curvilinear tooth lengthwise”, Journal of
Mechanical Design. Volume: 138(6), 064501. 2016.
https://doi.org/10.1115/1.4033396
XXXIII. ZhengF., HuaL., HanX., LiB., ChenD., “Linkage model and
manufacturing process of shaping non-circular gears”, Mechanism
andMachine Theory. Volume: 96, Pages: 192 – 212. 2016.
https://doi.org/10.1016/j.mechmachtheory.2015.09.010
XXXIV. ZhengF., HuaL., HanX., LiB., ChenD., “Synthesis of indexing
mechanisms with non-circular gears”, Mechanismand Machine Theory,
Volume: 105, Pages: 108 – 128, 2016,
https://doi.org/10.1016/j.mechmachtheory.2016.06.019

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Abstract:

Glaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to further eye diseases such as myopia and cataracts, the influence of glaucoma can’t be cured; however, the disease ranked as 2􀯡􀯗 driving reason for blindness according to the organization of the health world. Among eye sickness anticipated to influence around 80 million individuals by 2020. Raising the fluid pressure well-known by intraocular pressure (IOP) is the prime cause of Glaucoma disorder .Diagnoses of glaucoma could be achieved through observing the adjustment in the structure of Optic Nerve Head (ONH) to get its features. The proposed methodology suggests to extract region of Interest (ROI) and blurred its red band to enable the segmentation of Optic Disc(OD); followed by inpainting blood vessels stage to facilitate the work of the next stage, which was segmentation of the Optic Cup(OC), the accuracy rate, sensitivity and specificity for detection OD segmentation was 94.7549%, 95.058%, and 95.93%, respectively. The accuracy rate, sensitivity, and specificity for OC segmentation 94.3254%, 0.7877%, 0.9848% respectively.

Keywords:

Optic Disc,Optic Cup,Drishti_GS,Retinal fundus,Glaucoma Diagnosis,

Refference:

I. Bhat SH, Kumar P. Segmentation of Optic Disc by Localized Active
Contour Model in Retinal Fundus Image. Smart Innovations in
Communication and Computational Sciences: Springer; 2019. p. 35-44.
II. Eladawi N, ElTanboly A, Elmogy M, Ghazal M, Fraiwan L, Aboelfetouh
A, et al., editors. Diabetic Retinopathy Early Detection Based on OCT
and OCTA Feature Fusion. 2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019); 2019: IEEE.
III. Guo Y, Zou B, Chen Z, He Q, Liu Q, Zhao R. Optic cup segmentation
using large pixel patch based CNNs. 2016. Available from:
https://ir.uiowa.edu/omia/2016_Proceedings/2016/17/ DOI:
10.17077/omia.1056
IV. Ho H, Tham Y-C, Chee ML, Shi Y, Tan NY, Wong K-H, et al. Retinal
nerve fiber layer thickness in a multiethnic normal Asian population: the
Singapore epidemiology of eye diseases study. 2019;126(5):702-711.
V. Jonas JB, Bergua A, Schmitz–Valckenberg P, Papastathopoulos KI,
Budde WMJIO, Science V. Ranking of optic disc variables for detection
of glaucomatous optic nerve damage. 2000;41(7):1764-1773.
VI. Lindeberg T, Li M-XJCV, Understanding I. Segmentation and
classification of edges using minimum description length approximation
and complementary junction cues. 1997;67(1):88-98.
VII. Stein SK, Barcellos A. Calculus and analytic geometry: McGraw-Hill
New York; 1992.
VIII. Sivaswamy J, Krishnadas S, Joshi GD, Jain M, Tabish AUS, editors.
Drishti-gs: Retinal image dataset for optic nerve head (onh)
segmentation. 2014 IEEE 11th international symposium on biomedical
imaging (ISBI); 2014: IEEE.
IX. Spaeth GL, Henderer J, Liu C, Kesen M, Altangerel U, Bayer A, et al.
The disc damage likelihood scale: reproducibility of a new method of
estimating the amount of optic nerve damage caused by glaucoma.
2002;100:181.
X. Suha Dh. Athab NHS. Localization of Optic Disc in Retinal Fundus
Image Using Appearance Based Method and Vasculature Convergence.
IJS. 2020;61(1).

XI. Thakur N, Juneja MJESwA. Optic disc and optic cup segmentation from
retinal images using hybrid approach. 2019;127:308-322.
XII. Weinreb RN, Bowd C, Moghimi S, Tafreshi A, Rausch S, Zangwill LM.
Ophthalmic Diagnostic Imaging: Glaucoma. High Resolution Imaging in
Microscopy and Ophthalmology: Springer; 2019. p. 107-134.
XIII. Xue L-Y, Lin J-W, Cao X-R, Zheng S-H, Yu LJJoC. Optic Disk
Detection and Segmentation for Retinal Images Using Saliency Model
Based on Clustering. 2018;29(5):66-79.
XIV. Yin F, Liu J, Wong DWK, Tan NM, Cheung C, Baskaran M, et al.,
editors. Automated segmentation of optic disc and optic cup in fundus
images for glaucoma diagnosis. 2012 25th IEEE international symposium
on computer-based medical systems (CBMS); 2012: IEEE.
XV. Yu D-Y, Cringle SJ, Morgan WH. Glaucoma Related Ocular Structure
and Function. Medical Treatment of Glaucoma: Springer; 2019. p. 1-31.

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Abstract:

Segmentation has vital employment in regression analysis where data have some change point. Traditional estimation methods such as Hudson, D.J.;(1966) and Muggeo, V. M., (2003)have been reviewed. But these methods do not take into account robustness in the presence of outliers values. However, third method was used as rank-based method, where the analysis will be devoted to the ranks of data instead of the data themselves. Our contribution in this paper is to use M-estimator methodology with three distinct weight functions (Huber, Tukey, and Hampel) which has been combined with Muggeo version approach to gain more robustness, Thus we get robust estimates from the change point and regression parameters simultaneously. We call our new estimator as robust Iterative Rewrighted Mestimator: IRWm-method with respect to its own weight function. Our primary interest is to estimate the change point that joins the segments of regression curve, and our secondary interest is to estimate the parameters of segmented regression model. The real data set were used which concerned to bed-loaded transport as dependent variable (y) and discharge explanatory variable (x). The comparison has been conducted by using several criteria to select the most appropriate method for estimating the change point and the regression parameters. The superior results were marked for IRWm-estimator with respect to Tukey weight function.

Keywords:

Segmented regression,change point,rank-based estimator,iterative reweighted least squares,M-estimator,

Refference:

I. Almetwally, E.M., Almongy, H.M., (2018).”Comparison Between Mestimation,
S-estimation, And MM Estimation Methods of Robust Estimation
with Application and Simulation”. International Journal of Mathematical
Archive.Vol.9,No.11,pp 55-63.
II. Asuero, A.G., Gonzalez, G., Pablos, F.D., Ariza, J.L.G.,(1988).
“Determination of the optimum working range in spectrophotometric
procedures”. Talanta;531–537.
III. Blicschke, W., (1961).”Least Squares Estimators of Two Intersecting Lines”.
Technical Report, No. 7, Department of Navy, Office of Naval Research,
Contract No. Nonr-401(39), Project No. NR 042-212, BU-135-M(1):1–10.
IV. Das, R.,Banerjee, M.,Nan, B. and Zheng, H., (2015).”Fast estimation of
regression parameters in a broken-stick model for longitudinal data”. Journal
of the American Statistical
Association,http://dx.doi.org/10.1080/01621459.2015.1073154.
V. Fasola, S. ,Muggeo,V.M.R. and Küchenhoff,H.,(2017).”A heuristic, iterative
algorithm for change-point detection in abrupt change models”.
Computational Statistics,Vol.33, Issue 2,pp 997-1015.

VI. Greene, M.E. ,Rolfson, O., Garellick, G., Gordon, M. and Nemes, S. , (2014).
“Improved statistical analysis of pre- and post-treatment patient-reported
outcome measures (PROMs) the applicability of piecewise linear regression
splines”. Quality of Life Research, Vol.24,Issue3,pp.567–573.
VII. Hettmansperger, T., McKean, J.W., (2011).”Robust Nonparametric Statistical
Methods”. 2nd Ed. New York, Chapman.
VIII. Hudson, D.J.,(1966).”Fitting Segmented Curves Whose Join Points Have to
be Estimated” . Journal of the American Statistical Association , Vol. 61, No.
316, pp.(1097-1129).
IX. Jaeckel, L. A., (1972).”Estimating regression coefficients by minimizing the
dispersion of the residuals”. The Annals of Mathematical
Statistics,Vol.43,No.5, pp1449–1458.
X. Jureckova, J., (1971).”Nonparametric estimate of regression coefficients”.
The Annals of Mathematical Statistics,Vol.42,No.4,pp1328–1338.
XI. Küchenhoff, H.,(1997).”An exact algorithm for estimating breakpoints in
segmented generalized linear models”. Computational Statistics; 12:235–247.
XII. Lain, M.,(2018).”JoinpointRegrese”.Univerzita Karlova, Matematickofyzikální
fakulta, BAKALÁŘSKÁ
PRÁCE.http://hdl.handle.net/20.500.11956/100141.
XIII. Maciak, M., Mizera, I.,(2016).”Regularization techniques in joinpoint
regression”. Statistical Papers,Vol. 57, Issue 4, pp 939–955.
XIV. Martin, J.,Martin, G.D., Asuero, A.G.,(2017).”Intersecting Straight Lines
Titrimetric Applications”.
Intechopen,http://dx.doi.org/10.5772/intechopen.68827.
XV. McKean, J. W. , Hettmansperger, T. P.,(2016).”Rank-Based Analysis of
Linear Models and Beyond: A Review”.In:Robust Rank-Based and
Nonparametric Methods; Liu, R.Y.; McKean, J.W., Eds.; Springer
International Publishing; pp. 1–24.
XVI. Muggeo, V. M. R.,(2003).”Estimating regression models with unknown
breakpoints”.Statistics in Medicine,Vol.22,Issue19,pp3055–3071.
XVII. Muggeo, V. M. R.,(2017).”Interval estimation for the breakpoint in
segmented regression: a smoothed score-based approach”. Aust. N. Z. J. Stat.
59(3), 311–322.
XVIII. Orenti, A., Marubini, E.,(2019).”Robust regression analysis: a useful two
stage procedure”.Communications in Statistics – Simulation and
Computation,https://doi.org/10.1080/03610918.2018.1547400.
XIX. Osada,E., Borkowski,A., Sośnica,K., Kurpiński,G. Oleksy,M. and Seta,M.,
(2018).” Robust fitting of a precise planar network to unstable control points
using M-estimation with a modified Huber function”.J. Spat. Sci. 63 (1): 35–
47. https://doi.org/10.1080/14498596.2017.1311238.
XX. Quandt, R.E., (1958).”The estimation of the parameters of a linear regression
system obeying two separate regimes”. J. Amer. Statist. Assoc. 53,873–880.
XXI. Robison, D.,(1964).”Estimates for the Points of Intersection of Two
Polynomial Regressions”.Journal of the American Statistical Association, 59,
214-224.

XXII. Roslyakova, I.,(2017).”Modeling thermodynamical properties by segmented
non-linear regression”.Ruhr-University Bochum, Doctoral Thesis.
XXIII. Ryan, S. E. and Porth, L. S.,(2007).”A Tutorial on the Piecewise Regression
Approach Applied to Bedload Transport Data”. Report No. RMRS-GTR-189,
Department of Agriculture, Forest Service, Rocky Mountain Research
Station.
XXIV. Salini, S.,Cerioli, A.,Laurini, F. and Riani, M.,(2016).”Reliable Robust
Regression Diagnostics”. International Statistical Review, 84(1):99–127.
XXV. Shi, S., Li, Y., Wan, C. (2018).”Robust continuous piecewise linear
regression model with multiple change points”.The Journal of
Supercomputing,https://doi.org/10.1007/s11227-018-2597-x.
XXVI. Wang,N.,Wang,Y., Hu,S., Hu,Z., Xu,J., Tang,H., Jin,G.,(2018).”Robust
Regression with Data-Dependent Regularization Parameters and
Autoregressive Temporal Correlations”. Environmental Modelling &
Assessment, Vol.23, Issue 6, pp779–786.
XXVII. Yan, Y., Zhang, F., Zhou, X. (2017).”A note on estimating the bent line
quantile regression”.Comput Stat,32:611–630.
XXVIII. YU, B., Barrett, M. J., Kim, H.-J. and Feuer, E. J., (2007).”Estimating
joinpoints in continuous time scale for multiple change-point models”.
Comput. Statist. Data Anal., 51:2420–2427.
XXIX. Zhang, F., Li, Q.,(2017).”Robust bent line regression”. J. Statist. Plann.
Inference, Vol.185,pp41-55.
XXX. Zhang, F.,Shen, F.,Yang, T. and Li, Q.,(2018).”Segmented correspondence
curve regression model for quantifying reproducibility of high-throughput
experiments”.arXiv:1807.00943v1 [stat.ME].

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Abstract:

In this work, γ-Al2O3powder as copper adsorbent material was synthesized from polyoxohydroxide aluminum (POHA) precursor using aluminum powder, potassium hydroxide and d- glucose dissolved in distilled water and ethanol, and calcined at 500 °C. Prepared γ-Al2O3powder was characterized using (XRD), (FTIR), (SEM), (LGI) and (BET) method. The copper ions concentration of oilfieldproducedwater was determined using (ICP-OES). The parameters considered asγ-Al2O3adsorbent dose, adsorption time and pH used in this work are (0.2 and 0.4 mg/l), (30-180 min) and (4-10 pH) respectively. The characterization results showed that γ-phase is the dominant phase of synthesized Al2O3 powder. The results also showed that high adsorption performance for copper ions with a high removal efficiency of 99.99% using synthesized γ-Al2O3was obtained with an absorbed dose of 0.4 mg /l, adsorption time of 90 min and a pH of 7.

Keywords:

γ-alumina,chemical method,adsorption,copper adsorption,removal efficiency,

Refference:

I. Al-Haleem .A. A., Abdulah . H. H., Saeed .E. A., Components and
treatments of 36 oilfield produced water., Al Engineering Journal 2010; 6 (1) : 24
II. Al-Haleem .A. A., Razaq. A.H., Oilfield produced water management: treatment, 40, 1 – 9.
III. Arbabi.M., Golshani .,Removal of Copper Ions Cu (II) from
Industrial Wastewater:A Review of Removal Methods,International
Journal of Epidemiologic Research, 2016; 3(3): 283-293.
IV. Alvarez-Silva .M.,Uribe-Salas. A., Mirnezami.M. Mirnezami., Finch
.J., The point of zero charge of phyllosilicate minerals using the
Mular–Robertstitration technique, Minerals Engineering ,2010,23 (5) :383–389.
V. Hardi .M ., Siregar .Y. I., Anita .S., and Ilza .M., International
Conference of 38 Chemistry, in Journal of Physics: Conf. Series, 2019, pp. 1-6.
VI. Kanakaraju .D.,Ravichandar. S., Lim.Y.C ., Combined effects of
adsorption and 13 photocatalysis by hybrid TiO2/ZnO-calcium alginate
beads for the removal of 14 copper., Journal of Environmental Sciences
2017, 55 (1) : 214-223.
VII. 8Lueangchaichaweng.W,Singh.B ,Mandelli.D, Carvalho.W, Fiorilli .
S,Pescarmona.P, High surface area, nanostructured boehmite and alumina
catalysts: Synthesis andapplication in the sustainable epoxidation of
alkenes,Applied Catalysis A: General.2019;571(1):180-187.Volume
571, 5 February 2019, Pages 180-187.
VIII. Nassef E., El-Taweel. Y.A., Removal of copper from wastewater by
cementation 14 from simulated leach liquors J. Chem. Eng. Process
Technol 2015; 6(1) : 1-6.
IX. Segala .F, Correab .M,BacanieR,CastanheiracB, Politid M,Brochsztainc
S,Tribonia E,A Novel Synthesis Route of Mesoporous γ-Alumina from
Polyoxohydroxide Aluminum, Materials Research. 2018; 21(1):1-8
X. Visa. M., Synthesis and characterization of new zeolite materials
obtained from fly 15 ash for heavy metals removal in advanced
wastewater treatment. Powder Technol16 2016 ; 294(2) :338-374.
XI. Yang .J., HouBaohong., Wang J., Tian Beiqian., Bi.J., Wang.N., Li. X.,
and Huan.,Nanomaterials for the Removal of Heavy Metals from
Wastewater, Nanomaterials, 2019, 9(3):1-39.

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Abstract:

The optimal prediction or forecasting of time series values from the observations required many things such as checking the identification accuracy, model diagnosis, and data free from violations (outliers, for instance). Therefore, the researchers are always wondering if the used model or the supported method is sufficient to represent the data or there are more information that can be provided and probable increasing of precision as a consequence in the forecasting. This paper is an attempt to propose a new hybrid model building that can be denoted by AR-Holt (p+5). Also, suggest a new algorithm to estimate the parameters of this new hybrid model with its forecasting for inside and outside the series. Furthermore, the comparison has been done between this new hybrid model with AR(p) model which was identified as well as its parameters were estimated by many traditional methods which are Yule-Walker, Burg, robust RA, LS, Mcov and LMS methods for contaminated time series data. Simulation experiments have been conducted with different levels of contamination (p=0, 0.05, 0.15) to evaluate the superior of the performance of this new model according to different sample sizes (n=30, 70, 150). A real data application of the barley crops in Iraq is taken into consideration.

Keywords:

Yule-Walker method,Burg method,RA method,least squares,modified covariance,LMS method,autoregressive time series model,Holt,

Refference:

I. Abd El-Sallam, Moawad El-Fallah. “Methods of Estimation for
Autoregressive Models with Outliers”. Asian Journal of Mathematics and
Statistics, Vol. 6, No. 2, 2013.
II. Agricultural Statistics Directorate. “Wheat and Barley Products (1989-
2018)”. Technical Report, Central Statistical Organization (CSO), Iraq, 2019.
III. Bustos, Oscar H. and. Yohai Victor J. “Robust Estimates for ARMA
Models”. Journal of The American Statistical Association, Vol. 81, No. 393,
1986.
IV. Hamood, Munaf Y.. Jumaa, Ahlam A. and Mohana, Firas A. “Time Series
Analysis Part II”, Al-Dhad Publishing and Press, Baghdad, Iraq, 2019.
V. Kay, S. M. “Modern Spectral Estimation: Theory and
Application”. Englewood Cliffs, NJ: Prentice-Hall, 1988.
VI. Marple, S. L., Jr. “Digital Spectral Analysis with Applications”. Englewood
Cliffs, NJ: Prentice-Hall, 1987.
VII. Marple, S. L., Jr. “A Fast Computational Algorithm for the Modified
Covariance Method of linear Prediction”. Digital Signal Processing, Vol. 1,
1991.
VIII. Monson,H. “Statistical Digital Signal Processing and Modeling”. John Wiley
& Sons, 1996.
IX. Pukkila, Tarmo M. “An Improved Estimation Method for Univariate
Autoregressive Models”. Journal of Multivariate Analysis 27, 422-433, 1988.
X. Rousseeuw, Peter J. “Least Median of Squares Regression”. Journal of The
American Statistical Association, Vol. 79, No. 388, 1984.
XI. Xiao, Han and Wu, Wei Biao. “Covariance Matrix Estimation for Stationary
Time Series”. The Annals of Statistics, Vol. 40, No. 1, 466–493, 2012.
XII. Zhang, Hui-Min, and Duhamel, Pierre. “On The Methods for Solving Yule-
Walker Equations”. IEEE Transactions on Signal Processing, Vol. 40, No.
12, 1992.

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Abstract:

The prime aim of the current research is to investigate the thermal performance of the solar-assisted heat pump (SAHP) under the Iraqi climate experimentally and theoretically. In the winter season, the ambient air temperature reduces which causes a reduction in the coefficient of performance (COP) of heat pumps. By utilizing the thermal energy of solar to raise the heat transfer rate of the evaporator, compressor work diminishes and thus the COP of heat pump rises. The experimental setup of SAHP is perform by joining of a solar air heater and an air-toair heat pump. In this arrangement, the inlet of the air evaporator has been preheated by a solar air heater. The mathematical model based on energy balance was evolved and the performance of this system has been studied over a cold season of Baghdad city (placed in the middle of Iraq). The consequences revealed that the presence of porous media in the lower channel of the absorber plate providing a great surface area for convective heat transfer, therefore, the variation of air temperature and thermal efficiency of the solar air heater are raised. The average thermal efficiency for models (II, III, IV, and V) over the model I (Conventional) are (16.6%, 21.2%, 26.2%, and 30.3%) respectively at mass flow rate 0.02172 kg/s.

Keywords:

Air source Heat Pump,Solar Assisted Heat Pump,Power Consumption,

Refference:

I. Brumbaugh, J. E. (2004). Audel. HVAC Fundamentals. Volume 3: Air
Conditioning, Heat Pumps and Distribution Systems. Wiley Publishing.
105.
II. Caglar, A. and C. Yamalı, Performance analysis of a solar-assisted heat
pump with an evacuated tubular collector for domestic heating. Energy
and Buildings, 2012. 54(0): p. 22-28.
III. Chaturvedi, S. K., and Shen, J. Y., 1982, “Analysis of Two-Phase Flow
Solar Collectors with Application to Heat Pumps,” Journal of Solar
Energy Engineering, 104 pp. 358.
IV. Chaturvedi, S.K., D.T. Chen, and A. Kheireddine, Thermal performance
of a variable capacity direct expansion solar assisted heat pump. Energy
Conver Manage, 1998. 39(3-4): p. 181-191.
V. Dincer, I. K. (2010). Refrigeration Systems and Applications (2nd
Edition). New Jersey: Wiley.
VI. Duffie, J., & Beckman, W. (2006). Solar Engineering of Thermal
Processes. (3rd, Ed.) Hoboken, NJ: John Wiley & Sons, Inc.
VII. El-Sebaii, A. A., et al. “Investigation of thermal performance of-double
pass-flat and v-corrugated plate solar air heaters.” Energy 36.2 (2011):
1076-1086
VIII. Freeman, G. A., 1997, “Indirect Solar-Assisted Heat Pumps for
Application in the Canadian Environment,” Master’s thesis, Queen’s
University.
IX. Garg, H. P. Advances in Solar Energy Technology: Volume 3 Heating,
Agricultural and Photovoltaic Applications of Solar Energy. Springer
Science & Business Media, (1987).
X. Gorozabel Chata, F.B., S.K. Chaturvedi, and A. Almogbel, Analysis of
adirect expansion solar assisted heat pump using different
refrigerants.Energy Conversion and Management, 2005. 46(15-16): p.
2614-2624
XI. Hawlader MNA, Chou SK, Ullah MZ. The performance of a solar heat
pump water heating system. Appl Thermal Energy 2001; 21:1049.
XII. Jassim, Najim Abed, and Kadhim Kareem Al-Chlaihawi. “Experimental
Evaluation of Thermal Performance of Solar Assisted Vapor
Compression Heat Pump.” Journal of Engineering 21.11 (2015): 145-
160.
XIII. Kara O, Ulgen K, Hepbasli A. Exergetic assessment of direct-expansion
107. solar- assisted heat pump systems: review and modeling.
Renewable and Sustain- able Energy Reviews 2008;12(5):1383–401.

XIV. Kush, E. A., 1980, “Performance of Heat Pumps at Elevated Evaporating
Temperatures – with Application to Solar Input,” Journal of Solar Energy
Engineering, 102 pp. 203-210
XV. Li, Y.W., et al., Experimental performance analysis on a direct
expansion solar-assisted heat pump water heater. Applied
ThermalEngineering, 2007. 27(17-18): p. 2858-2868
XVI. Ong, K. S. “Thermal performance of solar air heaters Experimental
correlation.” Solar Energy 55.3 (1995): 209-220.
XVII. Smil, V., “Energy in Nature and Society: General Energetics of Complex
Systems”, (2008).
XVIII. Safijahanshahi, Esmaeel, and Mazyar Salmanzadeh. “Performance
simulation of combined heat pump with unglazed transpired solar
collector.” Solar Energy 180 (2019): 575-593.
XIX. Struckmann, Fabio. “Analysis of a flat-plate solar collector.” Heat and
Mass Transport, Project Report, 2008MVK160 (2008).
XX. Vladimir Soldo, Tonko Curko , Igor Balen ,Thermal Performance of a
Direct Expansion Solar Assisted Heat Pump, International Refrigeration
and Air Conditioning Conference. Paper 724.
XXI. Yousefi, M., & Moradali, M. (2015). Thermodynamic analysis of a
direct expansion solar assisted heat pump water heater. Journal of
Energy in Southern Africa, 26(2), 110-117.
XXII. Yumrutas, R. and O. Kaska, Experimental investigation of
thermalperformance of a solar assisted heat pump system with an energy
storage. International Journal of Energy Research, 2004. 28(2): p. 163-
75.

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