Archive

Abstract:

Malicious insiders are executing the severe attacks on cloud by misusing their privileges, which leads to the irreversible damages and loss of reputation. As the malicious insiders are authorized and integral part of the cloud, detecting and obstructing them to prevent the cloud from malicious attacks, became the complex and instantly focusable research aspect. An efficient “Insider Threat Detection Model” was proposed using the behavior reliance anomaly detection process. This paper elucidates Behavior Reliance Insider Threat Detection Model (BRITDM) implementation process and an empirical study was also conducted on the proposed model. Amazon AWS modeled log file input records were used as input to detect the insider activities, using the proposed Behavior Reliance Anomaly Detection (BRAD) four layer architecture. Detailed user and admin activities were collected from the cloud log files that are represented in JSON format. JSQL Parser used for the query knowledge extraction and to create XML Tree. SVM classifier is trained with Compact Prediction Tree (CPT) structures knowledge starts with the comparison of admin executed activity query knowledge against the respective CPT structures of design level activity base, to determine whether the executed admin activity is malicious or not according to the BRAD four layered architecture. Cloud BRITDM processed 30 input records and resulted 5 as unique activities, 5 as abnormal, 2 as unintended suspicious activities and one as intended insider thereat and reaming are normal activities. Experimental results shown the proposed BRITDM performed well in identifying the unique, abnormal, and suspicious and threats from insider activities.

Keywords:

ITDM,BRAD Process flow,Anomaly Detection,Malicious Insider Threat Detection,

Refference:

I. AWS CloudTrail: User Guide by Amazon AWS. Version-1, 2020, https://docs.aws.amazon.com/awscloudtrail/latest/userguide/awscloudtrail-ug.pdf

II. Bray, T. (2014). The JavaScript Object Notation (JSON) Data Interchange Format. RFC, 7158, 1-16

III. Cost of Insider Threats: Global Organizations,” https://www.observeit.com/ponemon-report-cost-of-insider-threats”

IV. Dawn Cappelli, Andrew Moore and Randall Trzeciak “The CERT Guide to Insider Threats”,Addison-Wesely,2012PearsonEducation, Inc.http://ptgmedia.pearsoncmg.com/images/9780321812575/samplepages/9780321812575.pdf

V. Eberle, William & Holder, Lawrence & Graves, Jeffrey. (2010). Insider Threat Detection Using a Graph-Based Approach. Journal of Applied Security Research. 6. 10.1080/19361610.2011.529413.

VI. Greitzer, F. L., &Hohimer, R. E. (2011). Modeling human behavior to anticipate insider attacks. Journal of Strategic Security, 4(2), 25

VII. Gueniche T., Fournier-Viger P., Raman R., Tseng V.S. (2015) CPT+: Decreasing the Time/Space Complexity of the Compact Prediction Tree. In: Cao T., Lim EP., Zhou ZH., Ho TB., Cheung D., Motoda H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science, vol 9078. Springer, Cham.

VIII. IBM X-Force Threat Intelligence Index Report “https://www.ibm.com/security/data-breach/threat-intelligence”

IX. Isaac Kohen, “2018 Crowd Research Partners ‘Insider Threat Report’: hopes and fears revealed”, 29 NOVEMBER 2017. http://crowdresearchpartners.com/wp-content/uploads/2017/07/Insider-Threat-Report-2018.pdf
X. Insider Threat Statistics for 2019: Facts and Figures : ”https://www.ekransystem.com/en/blog/insider-threat-statistics-facts-and-figures ”
XI. Jackson Project Home @github “https://github.com/FasterXML/jackson”

XII. Java Sql Parser, “http://jsqlparser.sourceforge.net/”.

XIII. K.VenkateswaraRao, Dr. T.Uma Devi “Architecture of Insider Threat Detection Model to Counter the Malicious Insider Threats on Cloud”, JASC: Journal of Applied Science and Computations – Volume 5, Issue 10, October/2018.

XIV. K.VenkateswaraRao, Dr. T.Uma Devi“Behavior Reliance Anomaly Detection with Customized Compact Prediction Trees”International Journal of Innovative Technology and Exploring Engineering (IJITEE)’, Volume-8 Issue-8, June 2019 https://www.ijitee.org/download/volume-8-issue-8/

XV. Kandias, Miltiadis&Virvilis, Nikos &Gritzalis, Dimitris. (2013). “The Insider Threat in Cloud Computing”. 6983. 93-103. 10.1007/978-3-642-41476-3_8.

XVI. P. Chattopadhyay, L. Wang and Y. Tan, “Scenario-Based Insider Threat Detection From Cyber Activities,” in IEEE Transactions on Computational Social Systems, vol. 5, no. 3, pp. 660-675, Sept. 2018.

XVII. S. Ceri and G. Gottlob, “Translating SQL Into Relational Algebra: Optimization, Semantics, and Equivalence of SQL Queries,” in IEEE Transactions on Software Engineering, vol. SE-11, no. 4, pp. 324-345, April 1985.

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Abstract:

Pipeline inspection robots are gaining importance and have seen several developments throughout the past decade. Developing a pipeline inspection robot can specifically overcome the issues of humans in labor and their intervention in an inconvenient condition during repair and maintenance inside the pipeline. This survey shows the advancements made in the field of pipeline inspection robots by classifying them according to their type of locomotion. The locomotion’s are divided into seven basic types and prototypes are developed based on these motions. Each prototype has its benefits and drawbacks based on their purpose of inspection. Different models are designed and validated for ensuring their functionality and performance. This review attempts to present the capabilities of various inspection robot models and compares their performance. This will provide insights into selection, developments and research gaps in this domain.

Keywords:

In-pipe robot,Pipelines,Mobile robots,Inspection,Shape adaptability,IPIR,

Refference:

I. Alnaimi FB, Mazraeh AA, Sahari KS, Weria K, Moslem Y. Design of a multi-diameter in-line cleaning and fault detection pipe pigging device. In2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS) 2015 Oct 18 (pp. 258-265). IEEE.
II. Brown L, Carrasco J, Watson S, Lennox B. Elbow Detection in Pipes for Autonomous Navigation of Inspection Robots. Journal of Intelligent & Robotic Systems. 2019 Aug 15;95(2):527-41.
III. Fang D, Shang J, Luo Z, Lv P, Wu G. Development of a novel self-locking mechanism for continuous propulsion inchworm in-pipe robot. Advances in Mechanical Engineering. 2018 Jan;10(1):1687814017749402.
IV. Ismail IN, Anuar A, Sahari KS, Baharuddin MZ, Fairuz M, Jalal A, Saad JM. Development of in-pipe inspection robot: A review. In2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT) 2012 Oct 6 (pp. 310-315). IEEE.
V. Kakogawa A, Ma S. Mobility of an in-pipe robot with screw drive mechanism inside curved pipes. In 2010 IEEE International Conference on Robotics and Biomimetics 2010 Dec 14 (pp. 1530-1535). IEEE.
VI. Kwon YS, Yi BJ. Design and motion planning of a two-module collaborative indoor pipeline inspection robot. IEEE Transactions on Robotics. 2012 Jan 31;28(3):681-96.
VII. Kim JH, Sharma G, Iyengar SS. FAMPER: A fully autonomous mobile robot for pipeline exploration. In2010 IEEE International Conference on Industrial Technology 2010 Mar 14 (pp. 517-523). IEEE.
VIII. Kim Kim HM, Suh JS, Choi YS, Trong TD, Moon H, Koo J, Ryew S, Choi HR. An in-pipe robot with multi-axial differential gear mechanism. In2013 IEEE/RSJ international conference on intelligent robots and systems 2013 Nov 3 (pp. 252-257). IEEE.
IX. Kakogawa A, Ma S. Robotic Search and Rescue through In-Pipe Movement. InAerial Robotic Systems 2019 Aug 12. IntechOpen.
X. Li P, Tang M, Lyu C, Fang M, Duan X, Liu Y. Design and analysis of a novel active screw-drive pipe robot. Advances in Mechanical Engineering. 2018 Oct;10(10):1687814018801384.
XI. Li T, Liu K, Liu H, Cui X, Li B, Wang Y. Rapid design of a screw drive in-pipe robot based on parameterized simulation technology. SIMULATION. 2019 Jul;95(7):659-70.
XII. Mazraeh AA, Ismail FB, Khaksar W, Sahari KS. Development of ultrasonic crack detection system on multi-diameter PIG robots. Procedia Computer Science. 2017 Jan 1;105:282-8.
XIII. Mohammed MN, Nadarajah VS, Lazim NF, Zamani NS, Al-Sanjary OI, Ali MA, Al-Youif S. Design and Development of Pipeline Inspection Robot for Crack and Corrosion Detection. In2018 IEEE Conference on Systems, Process and Control (ICSPC) 2018 Dec 14 (pp. 29-32). IEEE.
XIV. Qiao J, Shang J, Goldenberg A. Development of inchworm in-pipe robot based on self-locking mechanism. IEEE/ASME Transactions On Mechatronics. 2012 Feb 23;18(2):799-806.
XV. Ramirez-Martinez A, Rodríguez-Olivares NA, Torres-Torres S, Ronquillo-Lomelí G, Soto-Cajiga JA. Design and Validation of an Articulated Sensor Carrier to Improve the Automatic Pipeline Inspection. Sensors. 2019 Jan;19(6):1394.
XVI. Sawabe H, Nakajima M, Tanaka M, Tanaka K, Matsuno F. Control of an articulated wheeled mobile robot in pipes. Advanced Robotics. 2019 Oct 18;33(20):1072-86.
XVII. Savin S, Vorochaeva L. Footstep planning for a six-legged in-pipe robot moving in spatially curved pipes. In2017 International Siberian Conference on Control and Communications (SIBCON) 2017 Jun 29 (pp. 1-6). IEEE.
XVIII. Savin S, Jatsun S, Vorochaeva L. State observer design for a walking in-pipe robot. InMATEC Web of Conferences 2018 (Vol. 161, p. 03012). EDP Sciences.
XIX. Savin S, Vorochaev A, Vorochaeva L. Inverse Kinematics for a Walking in-Pipe Robot Based on Linearization of Small Rotations. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics. 2018;4:50-5.
XX. Savin S. RRT-based Motion Planning for In-pipe Walking Robots. In2018 Dynamics of Systems, Mechanisms and Machines (Dynamics) 2018 Nov 13 (pp. 1-6). IEEE.
XXI. Takagi M, Yoshida K, Hoshino H, Tadakuma R, Suzuri Y, Furukawa H. Sliding walk with friction control of double-network gel on feet of inchworm robot. Frontiers in Mechanical Engineering. 2019 Jul 19;5:44.
XXII. Venkateswaran S, Chablat D. A new inspection robot for pipelines with bends and junctions. InIFToMM World Congress on Mechanism and Machine Science 2019 Jun 30 (pp. 33-42). Springer, Cham.
XXIII. Wahed MA, Arshad MR. Wall-press type pipe inspection robot. In2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS) 2017 Oct 21 (pp. 185-190). IEEE.

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Abstract:

Railroad track inspection is essential to guarantee safe operation condition for the rails to travel on. Even though railway sector invests hefty costs, time and strong human workforce to ensure the performance and safety of the railroads, frequent accident occurs throughout the year due to poor visual inspection carried out by the human inspectors. The quality of inspection remains a question mark and deteriorates progressively when the experienced human inspectors are made to carry out the inspection all along the railroads exposing them to mental fatigue and other potential health hazards. Therefore, in this study, a simple method using visual perception and image processing techniques for the inspection of railroad track for anomalies is presented as an alternate solution to the traditional inspection system. An automated wheeled mobile robot is also prototyped to carry out the inspection on the railroads. This prototyped system uses a visual perception algorithm based on edge detection and feature extraction is developed in LabVIEW, which continuously records the images of the track; assesses and detects the railroad components such as loose bolts, bent boltsand surface cracks, which are very critical for rail safety. The performance of the proposed system is investigated in the laboratory conditions and results show high performance in the detection of railroad track anomalies.

Keywords:

Railroad Track Inspection,Visual Perception,Mobile Robot,Image Processing,Image Analysis,

Refference:

I. A. Distante, M. Nitti, E. Stella, P. L. Mazzeo, and F. Marino, “Automatic method and system for infrastructure visual inspection,” International Patent N. WO2007/010473, owned by the Italian National Research Council. World Intellectual Property Organization (WIPO), January 25, 2007 (International FilingDate: July 17, 2006; Priority Data: RM2005A000381, July 18, 2005).
II. A. Raza Rizvi, P. Rauf Khan, S. Ahmad., “Crack Detection in Railway Track Using Image Processing”, International Journal of Advance Research Ideas and Innovations in Technology, Vol.: 3, Issue: 4, pp. 489-496, 2017.
III. E.Resendiz, J.M.Hart and N.Ahuja., “Automated Visual Inspection of Railroad Tracks”, IEEE transactions on Intelligent Transportation Systems, Volume.: 14, Issue:2, pp. 751-760, 2013.
IV. E.Resendiz., L.Molina., J.Hart, J.Edwards, S.Sawadisavi, N.Ahuja and C.Barkan, “Development of a machine vision system for inspection of railway track components”, in Proceedings of the 12th WCTR World Conference on Transport Research, Lisbon, Portugal, pp.3355, 2010.
V. G.L. Foresti and C.S. Regazzoni, “New Trends in Video Communications, processing and Understanding in Surveillance Applications”, Proc. International Conference on Image Processing, Vol. : 89, Issue : 10, pp .1355 – 1367, 2001.
VI. H. Berger, “Non-Destructive Testing of Railroad Rail”, Transportation Research Record, Vol.: 744, pp. 22-26, 1980.
VII. Innotrack, D4.4.1, “Rail Inspection Technologies”, Projcet no. TIP5-CT-2006-031415, Available at: www.innotrack.net.
VIII. J.L. Rose, M.J. Avioli, P. Mudge and R. Sanderson, “Guidedwave inspection potential of defects in rail”, NDT&E International, Vol. : 37, pp153-161, 2004.
IX. K. Itoh, H. Tanaka and M. Seki, “Eye MovementAnalysis of Track Monitoring patterns of Night Train Operators :Effects of Geographic Knowledge and Fatigue”, in Proceedings of the IEA 2000/HFES Congress, pp. 360-363, 2000.
X. M. Karakose, O.Yaman, M. Baygin, K. Murat and E. Akin, “A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways”, International Journal of Mechanical Engineering and Robotics Research, Vol.:6, Issue:1, pp.22-27, 2017.
XI. M. Singh, S. Singh, J. Jaisal and J.Hempshall, “Autonomous Rail Track Inspection using Vision Based System”, IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, pp. 56 – 59, 2006.
XII. M.P. Papaelias and M. Lugg, “Detection and evaluation of rail surface defectsusingalternatingcurrentfieldmeasurement techniques”, Proceedings of the IMechE Part F : Journal of Rail and Rapid Transit, Vol.: 226, Issue: 5, pp. 530–541, 2016.
XIII. NI IMAQ Vision Assistance Tutorial [Online] Available: https://neurophysics.ucsd.edu/Manuals/National%20Instruments/NI%20Vision%20Assistant%20Tutorial.pdf
XIV. NI IMAQ Vision Manual [Online] Available : http://www.csun.edu/~rd436460/Labview/IMAQ-Manual.pdf
XV. NI Labview IMAQ Vision Concept Manual [Online]. Available: http://www.ni.com/pdf/manuals/322916b.pdf
XVI. P. L. Mazzeo, M. Nitti, E. Stella and A. Distante, “Visual Recognition of FasteningBolts for Railroad Maintenance”, Pattern Recognition Letters, pp. 669 – 677, 2004.
XVII. P. Yarza, A. Amirola, “New Technologies Applied to railway Infrastructure Maintenance”, Meeting on Planning, Design, Construction and Equipment of Metropolitan Railways, Madrid, 2003.
XVIII. R. A. Khan, S. Islam and R. Biswas, “Automatic Detection of defective rail anchors”, in Proc. IEEE 17th Internal Conferences on Intelligent Transportation Systems, pp.1583-1588, 2014.
XIX. R.K. Verma, A.Jeewan, S.Jain and M.Vats, “Automatic Railway Track Inspection for early warning using Real time image processing with GPS”, International Journal on Recent and Innovation Trends in Computing and Communication., Vol.:3, Issue:10, pp. 5880 – 5883, 2015.
XX. S. B. Aher and D. P. Tiwari, “Railway Disasters in India: Causes, Effects and Management”, International Journal of Reviews and Research in Social Science., Vol.: 6, Issue: 2, pp. 122-130, 2018.
XXI. S. Kenderian, B.B. Djordjevic, D. Cerniglia and G. Garcia, “Dynamic railroad inspection using the laser-air hybrid ultrasonic technique”, Insight, Vol.:48, Issue: 6, pp. 336-341, 2006.
XXII. S. Sawadisavi, J. Edwards, E.Resendiz, J. Hart., C. Barkan and N. Ahuja., “Development of a machine vision system for inspection of railroad track”, in Proceedings of the American Railway Engineering Maintenance Way Association Annual Conference, 2009.
XXIII. Y. Li, J. Wilson, and G.Y. Tian, “Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterization”, NDT & E International, Vol.:40, Issue.: 2, pp. 179-184, 2007.
XXIV. Z. Liu, A.D. Koffman, B. C. Waltrip and Y. Wang, “Eddy Current Rail Inspection Using AC Bridge Techniques”, Journal of Research of the National Institute of Standards and Technology, Vol.:118, pp.140-149, 2013.

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Abstract:

In this paper, Numerical investigation of the influence of inserting the metal foam to the solar chimney to induce natural ventilation in the test room is analyzed in this work. Two types of solar chimneys which without insertion of metal foam absorber and with insertion of metal foam absorber are designed with dimensions of length× width× air gap (2 m× 1 m×0.3 m) and size of the test room (1.5 m× 1.5 m×1 m). Four incline angles are tested (30^o,45^o,60^o,90^o) for each chimney and two length of tower inlet (30 cm, 40 cm). ANSYS FLUENT program (version 14.5) used to simulate this model and solve the governing equations by finite volume technique. The results showed that the air flow velocity at the outlet of ventilation solar chimney increases of the model with copper foam absorber about 33% from the model without copper foam absorber at constant inclination angle, therefore this gives indication of the important of insertion the copper foam as an absorber media in the ventilation solar chimney.

Keywords:

Solar Chimney,Low-Energy House,Ventilation,Metal Foam,Porous Media,ANSYS FLUENT,

Refference:

I. Abdallah, AmrSayed Hassan, et al. “Integration of evaporative cooling technique with solar chimney to improve indoor thermal environment in the New Assiut City, Egypt.” International Journal of Energy and Environmental Engineering 4.1 (2013): 45.‏
II. Aboulnaga, Mohsen M. “A roof solar chimney assisted by cooling cavity for natural ventilation in buildings in hot arid climates: an energy conservation approach in Al-Ain city.” Renewable Energy 14.1-4 (1998).
III. Ali Ghaffari and RaminMehdipour. Modeling and Improving thePerformance of Cabinet Solar Dryer Using Computational Fluid Dynamics.Int. J. Food Eng. 2015; 11(2): 157–172
IV. Ali, S.A.G. Study the effect of upstream riblet on wing- wall junction.M.Sc, Thesis, University of Technology, Iraq. 2011.
V. Bansal, N.K, R. Mathur, M.S. Bhandari,” A Study of Solar Chimney Assisted Wind Tower System for Natural Ventilation in Buildings”. Building and Environment, Vol. 29, 4, Pergamon Press., pp. 495-500, (1994).
VI. Bassiouny, Ramadan, and Nader SA Korah. “Effect of solar chimney inclination angle on space flow pattern and ventilation rate.” Energy and Buildings 41.2 (2009).‏
VII. Buonomo, Bernardo, et al. “Thermal and fluid dynamic analysis of solar chimney building systems.” International Journal of Heat and Technology 31.2 (2013).‏
VIII. Calmidi V. V., and Mahajan R. L. Forced convection in high porosity metalfoams. Journal of Heat Transfer, 2000; 122 (8): Õ 557.
IX. Guo C.X., Zhang W.J., and Wang D.B. Numerical investigation of heat transfer enhancement in latent heat storage exchanger withparaffin/graphite foam. 10th Int. Conf. on Heat Transfer, Fluid Mechanicsand Thermodynamics 2014; July: 14 – 26.
X. Hweij, WalidAbou, et al. “Evaporatively-cooled window driven by solar chimney to improve energy efficiency and thermal comfort in dry desert climate.” Energy and Buildings 139 (2017).‏
XI. Jianliu, X., Weihua, L.” Study on solar chimney used for room natural ventilation in nanjing”. Energy and Buildings, Vol.66, PP. 467-469, (2013).
XII. Kumar, MadhanAnand, and U. Krishnaveni. “Analysis of solar chimney with evaporative cooling cavity to improve indoor air quality.” Journal of Chemical and Pharmaceutical Sciences ISSN 974 (2015): 2115.‏
XIII. Rabani, Mehran, et al. “Empirical investigation of the cooling performance of a new designed Trombe wall in combination with solar chimney and water spraying system.” Energy and Buildings 102 (2015): 45-57.‏
XIV. Sarachitti, R., J. Hirunlabh, and J. Khedari. “3-D modelling of solar chimney-based ventilation system for building.” World Renewable Energy Congress VI. Pergamon, 2000.‏
XV. Struckmann, Fabio. “Analysis of a flat-plate solar collector.” Heat and Mass Transport, Project Report, 2008MVK160 (2008).‏

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Abstract:

In this research, for studying dynamic crack propagation behavior in thin plate, a modified method has adopted, when solidification with laser shot peening with cycling thermal effect have done. Since anew a technique is based on an accumulating two types of energies and employments, these together or alone by [Griffith] approach are used to emulate what happen in fuselage with specific conditions in order to study crack velocity and stress intensity factor. The two energies are coming from laser ray and cycling thermal. Analytical model has built with two scenarios for comparing between them. The first one (oven state) when cycling temperatures range for one cycle is from 30 to 150°C and the second (plane path state) when temperature range decreases from 30 to -30 °C  . In addition, the functions (cycling thermal) are functions of duration. Therefore, Fourier series method for periodic functions has built for cycling during path of flight. Oven state for a specific function has assumed with specific shape. Accordingly, simply support condition is adopted for all plates' edges. Laser ray influence has applied according to (P. Peyer & R. Fabbro) equations. For plane path state (cooling), it has been observed that the dynamic crack propagation clearly decreases when the energy of laser was influenced and cycling thermal has increased retardation of crack extension. While for oven state (heating), cycling thermal leads to reducing retardation of crack extension. Also, when comparing between two energies, a high benefit energy is produced from laser (positive effect), and thermal effect depends on state of system if heating or cooling and type of boundary conditions. The values are as well depended on thickness, crack ratio and properties of material

Keywords:

dynamic crack propagation,stress intensity factor,laser energy,thermal energy,

Refference:

I. Burns, J. T., et al. “Fatigue crack propagation of aerospace aluminum alloy 7075-T651 in high altitude environments.” International Journal of Fatigue 106 (2018): 196-207.
II. Duffy, Dean G. Advanced engineering mathematics with MATLAB. Crc Press, 2016.
III. E. Carrera, et al. “Simulation of shock wave impact due to explosion on a flying flexible aircraft.” Combustion, Explosion, and Shock Waves 43.6 (2007): 732-740.H.L.EWaldsand, R.J.H.Wanhill (1989)”Fracture mechanics”
IV. J. Zhang, et al. “Crack initiation and propagation mechanisms during thermal fatigue in directionally solidified superalloy DZ125.” International Journal of Fatigue 119 (2019): 355-366.
V. M. Sticchi, et al. “Review of residual stress modification techniques for extending the fatigue life of metallic aircraft components.” Applied Mechanics Reviews 67.1 (2015).
VI. Peyre, P., et al. “FEM simulation of residual stresses induced by laser peening.” The European Physical Journal-Applied Physics 23.2 (2003): 83-88.
VII. Peyre, P., et al. “Laser shock processing of aluminium alloys. Application to high cycle fatigue behaviour.” Materials Science and Engineering: A 210.1-2 (1996): 102-113.
VIII. R. A. Everett, et al. “The effects of shot and laser peening on fatigue life and crack growth in 2024 aluminum alloy and 4340 steel.” (2001).
IX. Syed, Abdul Khadar, et al. “Effect of temperature and thermal cycling on fatigue crack growth in aluminium reinforced with GLARE bonded crack retarders.” International Journal of Fatigue 98 (2017): 53-61.
X. Timoshenko, Stephen P., and Sergius Woinowsky-Krieger. Theory of plates and shells. McGraw-hill, 1959.
XI. Y. K. Gao, and X. R. Wu. “Experimental investigation and fatigue life prediction for 7475-T7351 aluminum alloy with and without shot peening-induced residual stresses.” Acta Materialia 59.9 (2011): 3737-3747.

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