Detection the Fulminant Position on Pulse Changing-State of Porous Material


  • Jeong-lae Kim Department of Biomedical Engineering, Eulji University, Seongnam, Korea
  • Kyu-dong Kim Department of Medical IT, Eulji University, Seongnam, Korea


Boom awakening level, Boom-awakening-imagery, Fulminant-awakening system, Fulminant shock


The boom changing-state technique is to be immixture the adjacent-angle fulminant-shock status of the glaring-classification awakening level (GCAL) on the boom awakening. Awakening imagery system for boom awakening condition is associated with the fulminant-shock condition, to inspection a background of glaring dot, that are found of the boom value with background dot by the fulminant form. Concept of awakening is associated to reference of glaring-classification level with changing-state by boom shock system on the porous material. Symbolizing-angle of adjacent changing-state of GCAL of the medium-minimum by fulminant-shock imagery, boom background dot was the boom value of the far changing-state of the Bo-AI-FA-φMED-MIN with (5.80±1.20) units, boom value of changing-state of the Bo-AI-CO-φMED-MIN with (4.06±(-0.04)) units, boom value of the changing-state of the Bo-AI-BO-φMED-MIN with 0.91±0.07 units, boom value of changing-state of the Bo-AI-VI-φMED-MIN with 0.18±(-0.03) units. Fulminant shock is look into adjacent-angle with the fulminant-shock imagery by background the boom awakening level on the GCAL. We are supply the glaring-classification imagery with the awakening level system. So, We can possible to imagery from classification and using boom data of fulminant shock awakening system.


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Tokuda E., Pedrini H. and Rocha A., Computer generated images vs. digital photographs: a synergetic feature and classifier combination approach. J Vis Commun Image Resupplied, 24(8) (2013), 1276–92.

Ng T., Physics-motivated features for distinguishing photographic images and computer graphics. In: 13th Proc. ACM int. conf. Multimedia, (2005), 239–248.

Pepsi M, B. B. ., V. . S, and A. . A. “Tree Based Boosting Algorithm to Tackle the Overfitting in Healthcare Data”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 5, May 2022, pp. 41-47, doi:10.17762/ijritcc.v10i5.5552.

Pan F. et al. Discriminating between photorealistic computer graphics and natural images using fractal geometry. Sci China Ser F: Inf Sci, 52 (2009), 329–37.

Farid H. and Lyu S., Higher-order wavelet statistics and their application to digital forensics. IEEE Comput Vision Pattern Recogn Workshop, 8 (2003), 94–101.

Agarwal, D. A. . (2022). Advancing Privacy and Security of Internet of Things to Find Integrated Solutions. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 05–08.

Voss R.F., Fractals in nature: from characterization to simulation. in: H.O. Peitgen, D. Saupe (Eds.), The Science of Fractal Images, Springer, New York, (1988), 21–70.

Sharma, V. N., & Hans, D. A. . (2022). A Study to Reconnoitering the dynamics of Talent Management Procedure at Hotels in Jharkhand. International Journal of New Practices in Management and Engineering, 11(01), 41–46.

V301-1,302-1,306-p// Kim J.L., Choi J.S., and Hwang K.S., A Study on Anticipation System of Shudder Distinction by the Physical Shape Alteration in Static Condition. The Journal of IIBC (JIIBC), 17(3) (2017), 115-120. DOI 10.7236/JIIBC.2017.17.3.115

Kim J.L. and Kim K.D., Prediction of shiver differentiation by the form alteration on the stable condition. International Journal of Internet Broadcasting and Communication (IJIBC), 9(4) (2017), 8-13. DOI 10.7236/IJIBC.2017.9.4.8

Paithane, P. M., & Kakarwal, D. (2022). Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 98–104.

Kim J.L. and Hwang K.S., Study of quake wavelength of dynamic changing-state with posture. International Journal of Advanced Smart Convergence (IJASC), 4(1), (2015), 99-103.

Kim J.L. and Kim K.D., Denoteation of central motion techniques: limpness motion function and limpness sensory unit function. International Journal of Advanced Culture Technology (IJACT), 4(3) (2016), 56-61. DOI 10.17703/IJACT.2016.4.3.56

Huiting J., Flisijn H., Kokkeler A.B.J. and Smit G.J.M., Exploiting phase measurements of EPC Gen2 RFID structures. IEEE Int Conf RFID-Technol Appl (RFID-TA), (2013), 1–6.

Bekkali A., Zou S..C, Kadri A., Crisp M. and Penty R.V., Performance analysis of passive UHF RFID systems under cascaded fading channels and interference effects. IEEE Trans Wirel Commun., 14(3) (2015), 1421–33.

DiGiampaolo E. and Martinelli F., Mobile robot localization using the phase of passive UHF RFID signals. IEEE Trans Ind Electron, 61(1) (2014), 365–76.

López Y. Á., Gómez M.E. and Andrés F.L.H., A received signal strength RFID-based indoor location system, Sensors and Actuators A, 255 (2017), 118–133.

Chiba, Z., El Kasmi Alaoui, M. S., Abghour, N., & Moussaid, K. (2022). Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm. International Journal of Communication Networks and Information Security (IJCNIS), 14(1).

Chawla K., McFarland C., Robins G. and Shope C., Real-time RFID localization using RSS, in: 2013 International Conference on Localization and GNSS (ICL-GNSS), Turin (Italy), (2013)(25–27 June), 1–6.

Glaring-classificationfunctionassociated boom awakeninglocation on the material




How to Cite

Kim, J.- lae ., & Kim, K.- dong . (2022). Detection the Fulminant Position on Pulse Changing-State of Porous Material. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 125 –. Retrieved from



Research Article