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Kodai Sato,Hirokazu Madokoro,Takeshi Nagayoshi,Shun Chiyonobu,Paolo Martizzi,Stephanie Nix,Hanwool Woo,Takashi K. Saito,Kazuhito Sato 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
This study was conducted to classify outcrop images using semantic segmentation methods based on deep learning algorithms. Carbon capture and storage (CCS) is an epoch-making approach to reduce greenhouse gases in the atmosphere. This study specifically examines outcrops because geological layer measurements can lead to production of a highly accurate geological model for feasible CCS inspections. Using a digital monocular RGB camera, we obtained 13 outcrop images annotated with four classes along with strata. Subsequently, we compared segmentation accuracies with changing input image sizes of three types and semantic segmentation methods of four backbones: SegNet, U-Net, ResNet-18, and Xception-65. The ResNet-18 and Xception-65 backbones were implemented using DeepLabv3+. Experimentally obtained results demonstrated that data expansion with random sampling improved the accuracy. Regarding evaluation metrics, global accuracy and local accuracy are higher than the mean intersection over union (mIoU) for our outcrop image dataset with unequal numbers of pixels in the respective classes. These experimentally obtained results revealed that resizing for input images is unnecessary for our method.
Operation and Maintenance of In-Situ CO₂ Measurement System Using Unmanned Aerial Vehicles
Kohei Nomura,Hirokazu Madokoro,Takashi Chiba,Makoto Inoue,Takeshi Nagayoshi,Osamu Kiguchi,Hanwool Woo,Kazuhito Sato 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
The aim of this study is to actualize an in-situ measurement system of greenhouse gases using a UAV. This paper presents vertical profiles of CO₂ concentration as a measurement result with operation and long-term maintenance for a periodic flight tests. For this study, a new joint part with a single structure for improving strength of the sensor mount is developed. Moreover, routine maintenance to replace a router and periodic regulation of the analyzer is provided. After updating flight altitude permission over 150 m, we conducted flight tests up to 500 m. We compare individual linear correction and liner regression applied for vertical profiles of CO₂ concentration. We address that correction using linear regression has advantage to reduce burden for in-situ measurement.
Hirokazu Madokoro,Saki Nemoto,Stephanie Nix,Osamu Kiguchi,Atsushi Suetsugu,Takeshi Nagayoshi,Kazuhito Sato 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Air pollution causes various health problems and diseases. Long-term PM<SUB>2.5</SUB> monitoring and prediction of its occurrence and sources are necessary not only in global areas based on public monitoring stations but also in local areas using cost-effective sensor systems. For this study, we developed a sensor system to achieve simplified and high-frequency PM<SUB>2.5</SUB> measurements. We attempted to learn and to predict local PM<SUB>2.5</SUB> concentrations from observed data using long short-term memory (LSTM) as a dominant time-series feature learning network. For improving learning and prediction accuracy evaluated according to the root mean square error (RMSE), sensor calibration is performed using a higher sensor. Moreover, we strove to reduce RMSE by optimizing its five major parameters. Experimentally obtained results demonstrate that the prediction accuracy is improved gradually after calibration and parameter optimization. As an ablation experiment, five meteorological factors are imported externally to verify the factors which contribute to reducing RMSE. Results verify the strong effects of local pressure and temperature for training and relative humidity and temperature for testing as validation.