Welcome to Scholar Publishing Group

Kinetic Mechanical Engineering, 2022, 3(1); doi: 10.38007/KME.2022.030103.

Internal Combustion Engine in Agricultural Machinery Field Relying on Artificial Fish Swarm Algorithm

Author(s)

Frey Cheolgi

Corresponding Author:
Frey Cheolgi
Affiliation(s)

Ben Gurion Univ Negev, Dept Phys Therapy, Beer Sheva, Israel

Abstract

In the field of agricultural machinery, as the power source for the operation of mechanical equipment, the internal combustion engine (ICE) is the key assembly component of mechanical equipment. Due to its high thermal efficiency and strong power, it is widely used in agriculture, industry and other fields. However, the ICE has complex structure and poor working conditions, so it is very easy to fail in actual work. Once the key components of the equipment fail, the production is likely to be interrupted, causing significant economic losses and even catastrophic consequences. Therefore, this paper studies and analyzes the application and realization of ICE in agricultural machinery field (AMF) relying on AF swarm algorithm (AFSA). This paper briefly analyzes the ICE in the field of agricultural machinery and the basic AFSA, and discusses the concrete realization and steps of the AFSA; by analyzing the parameters of AFSA, the application and realization of ICE in AMF relying on AFSA are studied, which is of great significance to the development of ICE in AMF.

Keywords

Artificial Fish Swarm Algorithm, Agricultural Machinery Field, Internal Combustion Engine Application, Internal Combustion Engine Research

Cite This Paper

Frey Cheolgi. Internal Combustion Engine in Agricultural Machinery Field Relying on Artificial Fish Swarm Algorithm. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 1: 18-27. https://doi.org/10.38007/KME.2022.030103.

References

[1] Kot L S, Gite L P, Agarwal K N. Work-Related Injuries among Farm Workers Engaged in Agricultural Operations in India: A Cross-Sectional Study. Injury Prevention, 2022, 88(N):116-9.

[2] Mas F D, Massaro M, Calandra D, et al. Exploring Agricultural Entrepreneurship and New Technologies: Academic and Practitioners' Views. British Food Journal, 2022, 124(7):2096-2113. https://doi.org/10.1108/BFJ-08-2021-0905

[3] Yu Y, Hussein R, Zaharchuk G, et al. Automated Detection of Arterial Landmarks and Vascular Occlusions in Patients with Acute Stroke Receiving Digital Subtraction Angiography using Deep Learning. Journal of NeuroInterventional Surgery, 2022, 2018(N):1464-6.

[4] Muller C G, Cruz A D, Canale F. Green Innovation in the Latin American Agri-Food Industry: Understanding the Influence of Family Involvement and Business Practices. British Food Journal, 2022, 124(7):2209-2238. https://doi.org/10.1108/BFJ-09-2021-0994

[5] Jung Y, Cho M K. Impacts of Reporting Lines and Joint Reviews on Internal Audit Effectiveness. Managerial Auditing Journal, 2022, 37(4):486-518. https://doi.org/10.1108/MAJ-10-2020-2862

[6] Alam G M, Brescia V, Biancone P P, et al. Using Bibliometric Analysis to Map Innovative Business Models for Vertical Farm Entrepreneurs. British Food Journal, 2022, 124(7):2239-2261. https://doi.org/10.1108/BFJ-08-2021-0904

[7] Yadava R N, Sinha B. Enhancing Agro-Environment and Socio-Economic Condition of Rural Poor: The Case of Lupin Corporate Social Responsibility. Social Responsibility Journal, 2022, 18(4):825-838. https://doi.org/10.1108/SRJ-03-2017-0053

[8] Kot L S, Gite L P, Agarwal K N. Work-related injuries among farm workers engaged in agricultural operations in India: a cross-sectional study. Injury Prevention, 2022, 88(N):116-9.

[9] Nandi M L, Khandker V, Nandi S. Impact of Perceived Interactivity and Perceived Value on Mobile App Stickiness: An Emerging Economy Perspective. Journal of Consumer Marketing, 2021, 38(6):721-737. https://doi.org/10.1108/JCM-02-2020-3661

[10] Mohan M, Gupta S K, Kalra V K, et al. Topical Silver Sulphadiazine--A New Drug for Ocular Keratomycosis. British Journal of Ophthalmology, 2019, 72(3):192-5. https://doi.org/10.1136/bjo.72.3.192

[11] Mallin M, Stolz D C, Thompson B S, et al. In Oceans We Trust: Conservation, Philanthropy and the Political Economy of the Phoenix Islands Protected Area. Marine Policy, 2019, 107(SEP.):103421.1-103421.12. https://doi.org/10.1016/j.marpol.2019.01.010

[12] Rao\T A. Future and Scope of Wind Energy in India. Renewable & Sustainable Energy Reviews, 2019, 13(2):285-317.

[13] Blasch E, Pham T, Chong C Y, et al. Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(7):80-93. https://doi.org/10.1109/MAES.2020.3049030

[14] Jayaratne M, Silva D D, Alahakoon D. Unsupervised Machine Learning Based Scalable Fusion for Active Perception. IEEE transactions on automation science and engineering, 2019, 16(4):1653-1663. https://doi.org/10.1109/TASE.2019.2910508

[15] Habbouche H, Benkedjouh T, Amirat Y, et al. Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach. Entropy, 2021, 23(697):1-20. https://doi.org/10.3390/e23060697

[16] Blank L, Meisinger J. Optimal Control of a Quasilinear Parabolic Equation and its Time Discretization. Applied Mathematics & Optimization, 2022, 86(3):1-19. https://doi.org/10.1007/s00245-022-09899-4

[17] Snow Z, Diehl B, Reutzel E W, et al. Toward In-Situ Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing Through Layerwise Imagery and Machine Learning. Journal of Manufacturing Systems, 2021, 59(10 October):12-26. https://doi.org/10.1016/j.jmsy.2021.01.008

[18] Sadasivuni S, Saha M, Bhatia N, et al. Fusion of Fully Integrated Analog Machine Learning Classifier with Electronic Medical Records For Real-Time Prediction of Sepsis Onset. Scientific Reports, 2022, 12(1):1-11. https://doi.org/10.1038/s41598-022-09712-w