Abstract
This paper belongs to the field of power detection technology. It mainly studies an intelligent electrical engineering measurement system, which mainly includes: solar power module, command input and output module, parameter setting module, single-chip measurement and control module, on-site numerical simulation module, optical detection module, fault detection module, display module. The invention can obtain cleaner and more effective sunlight through the solar power module, thereby saving energy, being more economical and environmentally friendly, being able to maintain power, and effectively preventing interruption in the electrical engineering measurement process; And it is more comprehensive than the measurement technology through the traditional fault detection module, which improves the effect of troubleshooting.
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Domingo-Ferrer, J., Farrà s, O., Ribes-González, J., Sánchez, D.: Privacy-preserving cloud computing on sensitive data: a survey of methods, products and challenges. Comput. Commun. (2019)
Koroniotis, N., Moustafa, N., Sitnikova, E.: Forensics and deep learning mechanisms for botnets in internet of things: a survey of challenges and solutions. IEEE Access (2019)
Ylmaz, E.N., Gnen, S.: Attack detection/prevention system against cyber attack in industrial control systems. Comput. Secur. (2018)
Al-rimy, B.A.S., Maarof, M.A., Shaid, S.Z.M.: Ransomware threat success factors, taxonomy, and countermeasures: a survey and research directions. Comput. Secur. (2018)
Deshmukh, S., Di Troia, F., Stamp, M.: Vigenère scores for malware detection. J. Comput. Virol. Hacking Tech. (2) (2018)
Coutinho, M., de Oliveira Albuquerque, R., Borges, F., Villalba, L.J.G., Kim, T.-H.: Learning perfectly secure cryptography to protect communications with adversarial neural cryptography. Sensors (5) (2018)
Shah, S.A.R., Issac, B.: Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Gener. Comput. Syst. (2018)
Vijayakumar, P., Chang, V., Jegatha Deborah, L., Balusamy, B., Shynu, P.G.: Computationally efficient privacy preserving anonymous mutual and batch authentication schemes for vehicular ad hoc networks. Future Gener. Comput. Syst. (2018)
Burg, A., Chattopadhyay, A., Lam, K.Y.: Wireless communication and security issues for cyber–physical systems and the Internet-of-Things. Proc. IEEE (1) (2018)
Gholizadeh, N., Saadatfar, H., Hanafi, N.: K-DBSCAN: an improved DBSCAN algorithm for big data. J. Supercomput. (2020). (prep)
Scitovski, R., Sabo, K.: A combination of k -means and DBSCAN algorithm for solving the multiple generalized circle detection problem. Adv. Data Anal. Classif. (2020). (prep)
Govender, P., Sivakumar, V.: Application of k -means and hierarchical clustering techniques for analysis of air pollution: a review (1980–2019). Atmos. Pollut. Res. (2020) (1)
Kang, J.S., et al.: Development of a systematic, self-consistent algorithm for the K-DEMO steady-state operation scenario. Nucl. Fus. (12) (2017)
Doostan, M., Chowdhury, B.H.: Power distribution system fault cause analysis by using association rule mining. Electr. Power Syst. Res. (2017)
Wang, A.L., Chen, B.X., Wang, C.G., Hua, D.: Non-intrusive load monitoring algorithm based on features of V–I trajectory. Electr. Power Syst. Res. (2018)
Saleem, Y., Crespi, N., Rehmani, M.H., Copeland, R.: Internet of Things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions. IEEE Access (2019)
Caballero, P., Banchs, A., de Veciana, G., Costa Perez, X., Azcorra, A.: Network slicing for guaranteed rate services: admission control and resource allocation games. IEEE Trans. Wirel. Commun. (10) (2018)
Nils, D., Fabian, K., Christian, W.: On the economic benefits of software-defined networking and network slicing for smart grid communications. NETNOMICS Econ. Res. Electron. Netw. (1–2) (2018)
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Zou, C. et al. (2023). Intelligent Power Measurement System. In: Hung, J.C., Chang, JW., Pei, Y. (eds) Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. IC 2023. Lecture Notes in Electrical Engineering, vol 1044. Springer, Singapore. https://doi.org/10.1007/978-981-99-2092-1_78
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DOI: https://doi.org/10.1007/978-981-99-2092-1_78
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