Development of USV Used in Underground Floors Surveying of the Contaminated Buildings at Fukushima Daiichi NPS

Sakaue, Tomoki; Nagakita, Toru; Kaneda, Tomoya; Yamashita, Yu; Nishizawa, Koju; Kanbara, Kenshi; Hanaoka, Hajime; Shirai, Shu; Kikuchi, Shiro; Uchijima, Daisaku

doi:10.1109/ssrr56537.2022.10018645

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…In addition to facilitating autonomy, location-tagged sensor data can be used for long-term environmental monitoring and analysis. In a recent study [8] published from the Fukushima Daiichi Nuclear Power Station in Japan, an investigation was conducted into the environmental conditions of an underground facility used for the temporary storage of contaminated water. Hundreds of sandbags filled with zeolite particles were deployed on the floor to remove radioactivity from the water, with plans for future decommissioning using a zeolite removal robot.…”

Section: A Motivationmentioning

confidence: 99%

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Cheng

Yao

Yang

et al. 2022

Sensors

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With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.

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Section: A Motivationmentioning

confidence: 99%

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Cheng

Yao

Yang

et al. 2022

Sensors

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Dynamical analysis of position-sensitive water-robot in radioactively contaminated areas by machine learning (ML) algorithm

Ho Woo

2023

Annals of Nuclear Energy

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Development of Zeolite Removal Robot for Decommissioning

Uchijima,

Tao,

Nishizawa

et al. 2024

JRM

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The transfer of stagnant water from the basement floor of the waste treatment building at the Fukushima Daiichi Nuclear Power Station is planned because of the possibility of water flowing out of the building if another tsunami occurs. However, the basement floor is installed with high-dose zeolite sandbags as adsorbents for radioactive materials, which renders their removal challenging. In this study, we conduct a field survey and field reproduction as well as develop a robot that can perform zeolite removal based on the results obtained. In addition, we perform verification tests in a full-scale mock-up test environment and extract issues for application to actual equipment.

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Development of USV Used in Underground Floors Surveying of the Contaminated Buildings at Fukushima Daiichi NPS

Cited by 4 publications

References 2 publications

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Dynamical analysis of position-sensitive water-robot in radioactively contaminated areas by machine learning (ML) algorithm

Development of Zeolite Removal Robot for Decommissioning

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