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您的检索式：作者名="Sagar A.S.M.Sharifuzzaman"
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| 1 | Relative-Position Estimation Based on Loosely Coupled UWB–IMU Fusion for Wearable IoT Devices显示文摘Relative positioning is one of the important techniques in collaborativerobotics, autonomous vehicles, and virtual/augmented reality (VR/AR)applications. Recently, ultra-wideband (UWB) has been utilized to calculaterelative position as it does not require a line of sight compared to a camerato calculate the range between two objects with centimeter-level accuracy.However, the single UWB range measurement cannot provide the relativeposition and attitude of any device in three dimensions (3D) because oflacking bearing information. In this paper, we have proposed a UWB-IMUfusion-based relative position system to provide accurate relative positionand attitude between wearable Internet of Things (IoT) devices in 3D. Weintroduce a distributed Euler angle antenna orientationwhich can be equippedwith the mobile structure to enable relative positioning. Moving average andmin-max removing preprocessing filters are introduced to reduce the standarddeviation. The standard multilateration method is modified to calculate therelative position between mobile structures. We combine UWB and IMUmeasurements in a probabilistic framework that enables users to calculatethe relative position between two nodes with less error. We have carried outdifferent experiments to illustrate the advantages of fusing IMU and UWBranges for relative positioning systems. We have achieved a mean accuracy of0.31m for 3D relative positioning in indoor line of sight conditions. | A.S.M.Sharifuzzaman Sagar Taein Kim Soyoung Park Hee Seh Lee Hyung Seok Kim | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 2 | Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet显示文摘The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Researchers are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer system.Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects.However,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint environment.As a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high accuracy.We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline.This study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet model.This study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect dataset.The experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models.The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications. | Yu Chen Sagar A.S.M.Sharifuzzaman Hangxiang Wang Yanfen Li L.Minh Dang Hyoung-Kyu Song Hyeonjoon Moon | 2023 | Computers, Materials & Continua2023，，6： | 0 |
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