CONCEPT OF INTELLIGENT SYSTEM OF GEOECOLOGICAL MONITORING
Keywords:
искусственный интеллект, машинное обучение, геоэкологический мониторинг, интеллектуальная система, датчики, автоматическая интерпретация данных, оптимизация, обнаружение аномалий, интерфейсные подсистемы, управление вводом/выводом данных, управление устройствами, пользовательский интерфейс, интеграция с другими системами.Abstract
This paper analyzes the use of artificial intelligence and machine learning in the field of geoecological monitoring. The various benefits of applying artificial intelligence are discussed, including data analysis, sensor design optimization, data detection and classification, and data anomaly detection. The paper analyzes the key functions of various subsystems, including data input / output management, devices, providing a user interface and data analysis. These functions are important for the effective interaction of the system with users and other components of the geoecological monitoring system, external databases and other monitoring systems. The concept of an intelligent system for geoecological monitoring is considered as an important tool for improving the quality of the analysis of geoecological processes and helping to solve urgent environmental problems. Ultimately, the introduction and use of intelligent monitoring systems can significantly contribute to more sustainable development and the achievement of environmental safety.
References
Разработка аппаратно-программных средств обеспечения малоглубинных инженерно-геофизических исследований: отчет о НИР (промежуточ.): / Институт машиноведения и автоматики НАН КР; рук. Брякин И. В.; исполн.: Бочкарев И. В. [и др.]. – Б., 2019. – 124 с. – № ГР 0007452.
S. Thrun, D. Fox, F. Burgard, Probabilistic Mapping Of An Environment By A Mobile Robot.IEEE International Conference on Robotics and Automation, 1998. Proceedings. 1546-1551 (1998)
Kim, J., Kim, M., Lee, MS. et al. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nat Commun 8, 14997 (2017). https://doi.org/10.1038/ncomms14997
Alan Mainwaring, David Culler, Joseph Polastre, Robert Szewczyk, and John Anderson. 2002. Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (WSNA '02). Association for Computing Machinery, New York, NY, USA, 88–97. https://doi.org/10.1145/570738.570751
Боев Н.М. Анализ командно-телеметрической радиолинии связи с беспилотными летательными аппаратами// Вестник Сибирского государственного аэрокосмического университета имени академика М.Ф.Решетнева. Выпуск 2 (42) / гл. ред. д.т.н. Ковалев И.В. – Красноярск: СибГАУ, 2012. – С.86–91.
I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks, Volume 38, Issue 4, 2002, Pages 393-422, ISSN 1389-1286, https://doi.org/10.1016/S1389-1286(01)00302-4.
Sharma, H.; Haque, A.; Blaabjerg, F. Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey. Electronics 2021, 10, 1012. https://doi.org/10.3390/electronics10091012
Aiswarya Raj Munappy, Jan Bosch, Helena Holmström Olsson, Anders Arpteg, Björn Brinne, Data management for production quality deep learning models: Challenges and solutions, Journal of Systems and Software, Volume 191, 2022, 111359, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2022.111359.
Tanveer Ahmad, Hongyu Zhu, Dongdong Zhang, Rasikh Tariq, A. Bassam, Fasee Ullah, Ahmed S AlGhamdi, Sultan S. Alshamrani, Energetics Systems and artificial intelligence: Applications of industry 4.0, Energy Reports, Volume 8, 2022, Pages 334-361, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2021.11.256.
Nambiar, A.; Mundra, D. An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management. Big Data Cogn. Comput. 2022, 6, 132. https://doi.org/10.3390/bdcc6040132
Maxat Kulmanov and others, Semantic similarity and machine learning with ontologies, Briefings in Bioinformatics, Volume 22, Issue 4, July 2021, bbaa199, https://doi.org/10.1093/bib/bbaa199
Zhang, Y.; Wang, G.; Li, M.; Han, S. Automated Classification Analysis of Geological Structures Based on Images Data and Deep Learning Model. Appl. Sci. 2018, 8, 2493. https://doi.org/10.3390/app8122493
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58 pages. https://doi.org/10.1145/1541880.1541882
Forootan, M.M.; Larki, I.; Zahedi, R.; Ahmadi, A. Machine Learning and Deep Learning in Energy Systems: A Review. Sustainability 2022, 14, 4832. https://doi.org/10.3390/su14084832
Ibrahim, A.M.; Yau, K.-L.A.; Chong, Y.-W.; Wu, C. Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms. Appl. Sci. 2021, 11, 10870. https://doi.org/10.3390/app112210870
Ghobadi F, Kang D. Application of Machine Learning in Water Resources Management: A Systematic Literature Review. Water. 2023; 15(4):620. https://doi.org/10.3390/w15040620
Cho S, Kim H-S, Kim H. Locally Specified CPT Soil Classification Based on Machine Learning Techniques. Sustainability. 2023; 15(4):2914. https://doi.org/10.3390/su15042914
C. Alippi et al., “Artificial intelligence for instruments and measurement applications,” IEEE Instrum. Meas. Mag., vol. 1, no. 2, pp. 9-17, Jun. 1998.
Benti, N.E.; Chaka, M.D.; Semie, A.G. Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability 2023, 15, 7087. https://doi.org/10.3390/su15097087
Hurtado Sánchez JA, Casilimas K, Caicedo Rendon OM. Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey. Sensors. 2022; 22(8):3031. https://doi.org/10.3390/s22083031
Брякин И.В., Лыченко Н.М. Применение объектно-ориентированного подхода к проектированию информационно-измерительных систем // Проблемы автоматики и управления – №1-2.– 2010.– стр. 188-192
Верзунов, С. Н. Способ оптимизации конструктивных параметров ячеек-резонаторов микрополосковых антенн на основе интеллектуального анализа данных / С. Н. Верзунов // Электротехнические системы и комплексы. – 2022. – № 3(56). – С. 54-64. – DOI 10.18503/2311-8318-2022-3(56)-54-64. – EDN GBBNJP.
Kondratenko, Y.; Atamanyuk, I.; Sidenko, I.; Kondratenko, G.; Sichevskyi, S. Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing. Sensors 2022, 22, 1062. https://doi.org/10.3390/s22031062
Xia, C.; Sugiura, Y. Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design. Sensors 2021, 21, 6893. https://doi.org/10.3390/s21206893
Mohsen Soori, Behrooz Arezoo, Roza Dastres,Artificial intelligence, machine learning and deep learning in advanced robotics, a review // Cognitive Robotics, Volume 3, 2023, Pages 54-70, ISSN 2667-2413, https://doi.org/10.1016/j.cogr.2023.04.001.
DeMedeiros, K.; Hendawi, A.; Alvarez, M. A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks. Sensors 2023, 23, 1352. https://doi.org/10.3390/s23031352
Dey, S.; Bhattacharya, R.; Schwenker, F.; Sarkar, R. Median Filter Aided CNN Based Image Denoising: An Ensemble Approach. Algorithms 2021, 14, 109. https://doi.org/10.3390/a14040109
Singh, A.K., Krishnan, S. ECG signal feature extraction trends in methods and applications. BioMed Eng OnLine 22, 22 (2023). https://doi.org/10.1186/s12938-023-01075-1
Sarker, I.H. AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN COMPUT. SCI. 3, 158 (2022). https://doi.org/10.1007/s42979-022-01043-x
Seng, J.K.P.; Ang, K.L.-m.; Peter, E.; Mmonyi, A. Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing. Electronics 2022, 11, 2239. https://doi.org/10.3390/electronics11142239
https://www.xenonstack.com/blog/data-preprocessing-wrangling-ml (дата обращения: 10.06.2023)
Raffaele Pugliese, Stefano Regondi, Riccardo Marini, Machine learning-based approach: global trends, research directions, and regulatory standpoints, Data Science and Management, Volume 4, 2021, Pages 19-29, ISSN 2666-7649, https://doi.org/10.1016/j.dsm.2021.12.002 (дата обращения: 11.06.2023)
Oprea, S.-V.; Bâra, A.; Puican, F.C.; Radu, I.C. Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption. Sustainability 2021, 13, 10963. https://doi.org/10.3390/su131910963
Модели прогноза уровня загрязнения атмосферного воздуха г. Бишкек / Н. М. Лыченко, Л. И. Великанова, С. Н. Верзунов, А. В. Сороковая // Вестник Кыргызско-Российского Славянского университета. – 2021. – Т. 21, № 4. – С. 87-95. – EDN IRIZPF.
S.C. Jong, D.E.L. Ong, E. Oh State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction, Tunnelling and Underground Space Technology, Volume 113, 2021, 103946, ISSN 0886-7798, https://doi.org/10.1016/j.tust.2021.103946.
Kuanishbay Sadatdiynov, Laizhong Cui, Lei Zhang, Joshua Zhexue Huang, Salman Salloum, Mohammad Sultan Mahmud A review of optimization methods for computation offloading in edge computing networks, Digital Communications and Networks, Volume 9, Issue 2, 2023, Pages 450-461, ISSN 2352-8648, https://doi.org/10.1016/j.dcan.2022.03.003
Abid Haleem, Mohd Javaid, Mohd Asim Qadri, Ravi Pratap Singh, Rajiv Suman, Artificial intelligence (AI) applications for marketing: A literature-based study, International Journal of Intelligent Networks, Volume 3, 2022, Pages 119-132, ISSN 2666-6030, https://doi.org/10.1016/j.ijin.2022.08.005.
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