Electrocardiograms (ECGs) are essential tools for the diagnosis and monitoring of heart diseases. Accurate and automatic detection of cardiac abnormalities from ECG signals is crucial for timely intervention and effec...
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This study addresses the challenge of manual soldering skill evaluation and transfer in the context of small and medium-sized enterprises where automation is less prevalent. Recognizing the limitations of current sing...
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The random forest, a machine learning technique, offers several preferable features that have drawn the interest and focus of researchers. In practically applying the random forest, we found that the training data con...
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In recent years, the field of I'CT education has emerged, and the research and development of educational support robots has attracted significant interest. In this study, we focus on a perplexity estimation metho...
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In Monocular Keyframe Visual Simultaneous Localization and Mapping (MKVSLAM) frameworks, when incremental position tracking fails, global pose has to be recovered in a short-time window, also known as short-term reloc...
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ISBN:
(纸本)9798350355376;9798350355369
In Monocular Keyframe Visual Simultaneous Localization and Mapping (MKVSLAM) frameworks, when incremental position tracking fails, global pose has to be recovered in a short-time window, also known as short-term relocalization. This capability is crucial for mobile robots to have reliable navigation, build accurate maps, and have precise behaviors around human collaborators. This paper focuses on the development of robust short-term relocalization capabilities for mobile robots using a monocular camera system. A novel multimodal keyframe descriptor is introduced, that contains semantic information of objects detected in the environment and the spatial information of the camera. Using this descriptor, a new Keyframe-based Place Recognition (KPR) method is proposed that is formulated as a multi-stage keyframe filtering algorithm, leading to a new relocalization pipeline for MKVSLAM systems. The proposed approach is evaluated over several indoor GPS denied datasets and demonstrates accurate pose recovery, in comparison to a bag-of-words approach.
Rapid climate change, humanity's need for wood, and other objective and subjective factors strongly affect the forest system of our planet. An analysis of existing global forest cover map products showed that the ...
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This study explores the potential of automating robotic laboratory readiness assessment by integrating Large Language Models (LLMs) with robotic dataacquisition. It investigates the capability of LLMs to detect equip...
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ISBN:
(纸本)9798350377712;9798350377705
This study explores the potential of automating robotic laboratory readiness assessment by integrating Large Language Models (LLMs) with robotic dataacquisition. It investigates the capability of LLMs to detect equipment motion and operational status using visual and auditory information. Despite the challenges LLMs face in spatial analysis, this study also investigates LLM grounding methods to ensure accurate workspace assessment. By inspecting a robotic cooking setup with camera-equipped robotic arm, LLMs can detect the motion of custom equipment via color-coded marks, and identify the operational status of kitchen appliances from a single image without any physical augmentations. Additionally, device operation perceived through the emission of loud noises can be assessed by post-processing sound recordings and analyzing loudness and sound frequency metrics presented in a visual plot form. For simple spatial tasks like saucepan positioning, LLM provides accurate assessments when grounded with a single image, while complex workspace safety assessment task requires extensive knowledge of past experiences. By reviewing status of each checklist item, the LLM can decide whether experiment needs to be halted or requires human intervention, offering a set of troubleshooting steps. These findings demonstrate feasibility of the self-assessment approach for robotic laboratory systems, paving the way for future deployments.
data-driven control, which embraces artificial intelligence, machine learning, and experience-based inferencing architectures, has gained significant interest for its ability to provide robust optimization in model-fr...
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ISBN:
(纸本)9798350355376;9798350355369
data-driven control, which embraces artificial intelligence, machine learning, and experience-based inferencing architectures, has gained significant interest for its ability to provide robust optimization in model-free, nonlinear, and time-varying paradigms. Traditional systems, such as the haptic paddle, used to communicate system dynamics principles in undergraduate curricula, have yet to be adapted to the memory and processing requirements of data-driven control. In this work, we present a modular, open-source 3D printable friction-driven haptic paddle design, building on the designs proposed by the community, using commercial components and simple microelectronic packaging, to enable robust data-driven control for integration in undergraduate education. We make use of the RP2040 microcontroller, a small light-weight logic platform capable of fast online computation and robust memory storage for onboard data-driven control. To validate our design, we first develop an experimental model of the physical dynamics that shows that our 3D printed friction drive is comparable with friction driven paddles and capstan-cable driven paddles. Further, we demonstrate the utility of our design in explicating data-driven control by presenting the development of basic machine learning and reinforcement learning architectures for online, model-free robust control in the presence of time-variable plant dynamics in a trajectory tracking task that is well suited for implementation in undergraduate and introductory graduate system dynamics and controls curricula.
This study aims to develop a system for detecting medical errors in unit dose packaging (UDP). The system compares the contents of subsequent UDPs to the first package in a series, which serves as a reference to ensur...
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Real-time data streaming pipelines are immensely valuable in today's data-driven world since they enable continuous data processing and analytics. This research paper provides a comprehensive exploration of the ar...
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