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Artificial intelligence enhanced environmental detection system
- 1 Teradyne (Shanghai) Co., Ltd.
* Author to whom correspondence should be addressed.
Abstract
This paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. At the heart of our approach lies the combination of a complex AI model with the hardware and software components of the inspection system. This combined approach can significantly improve the accuracy of detection systems through greater ability to predict environmental changes and events, underscoring the superior performance of hardware and software combined with AI technology. This paper delves into the details of hardware and software design, and discusses measurement implementation methods using a build-down machine. We also explore the practical application of AI models within the framework described above. In addition, this paper also describes the implementation of communication protocols to ensure the effective data exchange between the system network and the artificial intelligence model. These protocols are essential for the real-time processing and analysis of environmental data, enabling systems to respond quickly to detected changes.
Keywords
Artificial Intelligence, Environmental Detection System, Network Interface Chip
[1]. Guanyu Gao, Jie Li, Yonggang Wen (2020). DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning. IEEE Internet of Things Journal Volume: 7.
[2]. STMicroelectronics. (n.d.). 32-bit Arm Cortex MCUs. Retrieved March 9, 2024, from https://www.st.com/en/microcontrollers-microprocessors/stm32-32-bit-arm-cortex-mcus.html.
[3]. STMicroelectronics. (n.d.). Artificial intelligence ecology. Retrieved March 9, 2024, from https://www.stmcu.com.cn/ecosystem/app/ai
[4]. WIZnet. (n.d.). W5100. WIZnet. Retrieved March 9, 2024, from https://www.wiznet.io/product-item/w5100/
[5]. Weiting Zhang, Dong Yang, Hongchao Wang (2019). Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Systems Journal Volume: 13.
Cite this article
Wang,X. (2024). Artificial intelligence enhanced environmental detection system. Applied and Computational Engineering,66,156-159.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Functional Materials and Civil Engineering
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