The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental re...
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Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. process limitatio...
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Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has...
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Data-driven techniques show promising results in force estimation for Concentric Tube Continuum Robots, but often require extensive datasets, which are difficult to acquire. This work introduces a mapping-based transf...
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ISBN:
(数字)9798331520205
ISBN:
(纸本)9798331520212
Data-driven techniques show promising results in force estimation for Concentric Tube Continuum Robots, but often require extensive datasets, which are difficult to acquire. This work introduces a mapping-based transfer learning approach to improve the efficiency of data-driven methods for contact force estimation, by proposing a diffeomorphic mapping-based algorithm that reduces data requirements, enabling more practical application of these methods. By transforming data from pre-curved tubes into the feature space of non-curved tubes, our method allows a pre-trained neural network to estimate forces efficiently across various tube configurations, eliminating the need for additional data for changing tube configurations and retraining of the network. Simulation tests show high accuracy for curvatures up to $\kappa = 7\frac{1}{m}$, significantly reducing the need to create large datasets for each new robot configuration.
Due to the change of industrial processes and demographic shift in many countries, an increase in the use and application of exoskeletons is expected. However, design, development and deployment of exoskeletons requir...
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Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this ...
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Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of ...
Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of the robot is paramount. CTCRs are made up of thin elastic rods and are intended to be applied inside the human body, where conventional sensor-based measurements are not feasible. Consequently, research is resorting to estimate the forces through geometric, numeric, or optimization methods. However, these methods often suffer from slow convergence. In this paper, we introduce a novel data-driven approach for estimating contact forces along the body of a CTCR that offers an estimation precision comparable to the current state-of-the-art optimization-based approaches, but exhibits nearly two orders of magnitude faster convergence. The proposed method is scalable and exhibits a significant performance in response to a wide range of external forces. The approach was evaluated in simulations and on a real 2-tube CTCR.
The kinematic structure of Franka Emika's redundant cobot Panda features two translational offsets that prevent three of the six pairs of adjacent joints from intersecting each other. These offsets make Panda'...
The kinematic structure of Franka Emika's redundant cobot Panda features two translational offsets that prevent three of the six pairs of adjacent joints from intersecting each other. These offsets make Panda's elbow motion in null space hard to predict with respect to existing redundancy parameters. The null space motion analysis of Panda's elbow presented in this work leads to the definition of a redundancy parameter that can be used intuitively. The semi-analytical approach applied in this work induces a fast inverse kinematics algorithm that offers a redundancy resolution which does not affect the reachability of the given end effector pose. Even libfranka, Franka Emika's supplied library, does not offer such a Cartesian approach of keeping control of Panda's secondary motion while fulfilling primary manipulation tasks.
Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has ...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.
The emission of carbon oxides（COX）, nitrogen oxides（NOX）, sulfur compounds, and volatile organic compounds（VOCs） from vehicles has significantly impacted the air quality, thus posing threats to the environmental...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
The emission of carbon oxides（COX）, nitrogen oxides（NOX）, sulfur compounds, and volatile organic compounds（VOCs） from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared（NDIR） multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network（1D-CNN） and depthwise separable convolutional neural network（DS-CNN）. The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust.
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