Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, suc...
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Current linkage-driven prosthetic hands still show limitations in aspects such as the thumb design and fingertip sensor. Moreover, linkage-driven prosthetic hands still lack quantitative precision grasp quality. In th...
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Current linkage-driven prosthetic hands still show limitations in aspects such as the thumb design and fingertip sensor. Moreover, linkage-driven prosthetic hands still lack quantitative precision grasp quality. In this study, we developed a novel thumb structure with coupled abduction-adduction and pronation-supination movement in the trapeziometacarpal joint. We also developed a fully integrated fingertip tactile sensor with all components embedded in the distal phalanx designed to facilitate in-hand precision manipulation. Furthermore, we devised a new metric to evaluate the precision grasp quality based on the force conditions during grasp. On the basis of this metric, we optimized the geometry parameters of the thumb and index finger using the Monte Carlo method. The results show that, compared with the anthropomorphic trajectory measured from a human index, the proposed method improves the grasping ability by more than 10%. Finally, we developed a prototype prosthetic hand based on the proposed design methods and demonstrated by experiment that it was able to perform human-like thumb opposition and to pass both precision and power grasp tests.
In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficul...
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In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset
In this paper, the augment Kalman filter (AKF) approach is discussed under the infinity condition. It is proved that the AKF in infinity condition is equivalent to the minimum-variance unbiased (MVU) estimation propos...
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In this paper, the augment Kalman filter (AKF) approach is discussed under the infinity condition. It is proved that the AKF in infinity condition is equivalent to the minimum-variance unbiased (MVU) estimation proposed in Gillijns & De Moor [(2007). Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica, 43(1), 111-116]. This result can be regarded as a new contribution in the field of joint input and state estimation. Meanwhile, this result gives another way to estimate the state and unknown input simultaneously. Finally, the theoretical result is validated by simulations.
Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance ca...
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Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials;second, its computational cost is very low;and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.
Accurate tumor segmentation of multi-modality PET/CT images plays a vital role in computer-aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary information in multi-modality PET/CT ...
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Accurate tumor segmentation of multi-modality PET/CT images plays a vital role in computer-aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary information in multi-modality PET/CT segmentation. However, existing methods usually lack interpretability and fail to sufficiently identify and aggregate critical information from different modalities. In this study, we proposed a novel segmentation framework that incorporated an interpretation module into the multi-modality segmentation backbone. The interpretation module highlighted critical features from each modality based on their contributions to the segmentation performance. To provide explicit supervision for the interpretation module, we introduced a novel interpretation loss with two fusion schemes: strengthened fusion and perturbed fusion. The interpretation loss guided the interpretation module to focus on informative features, enhancing its effectiveness in generating meaningful interpretable masks. Under the guidance of the interpretation module, the proposed approach can fully exploit meaningful features from each modality, leading to better integration of multi-modality information and improved segmentation performance. Ablative and comparative experiments were conducted on two PET/CT tumor segmentation datasets. The proposed approach surpassed the baseline by 1.4 and 1.8 Dices on two datasets, respectively, indicating the improvement achieved by the interpretation method. Furthermore, the proposed approach outperformed the best comparison approach by 0.9 and 0.6 Dices on two datasets, respectively. In addition, visualization and perturbation experiments further illustrated the effectiveness of the interpretation method in highlighting critical features.
A three-level T-type inverter has higher efficiency and lower output voltage harmonics compared with the traditional two-level inverter. However, neutral-point voltage fluctuation and common-mode voltage (CMV) can neg...
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A three-level T-type inverter has higher efficiency and lower output voltage harmonics compared with the traditional two-level inverter. However, neutral-point voltage fluctuation and common-mode voltage (CMV) can negatively affect the performance of the three-level T-type inverter. This study proposes a novel hybrid variable virtual space vector ((HVSV)-S-2) strategy to mitigate this problem. Based on the virtual space vector scheme, new variable virtual small and medium vectors are reconstructed with the principle that the change of neutral-point charge of each virtual vector in a switching period is zero, and the adjustment factor is further added to the virtual vector to achieve the balance of the neutral-point voltage. At the same time, the vectors with a large CMV are eliminated to ensure that the CMV is minimised. Therefore, the (HVSV)-S-2 has excellent neutral-point voltage balance control capability and common-voltage suppression at both high- and low-modulation ratios. Theoretical analysis, simulations, and experimental results demonstrate the effectiveness and feasibility of the proposed strategy for full modulation ratios.
This paper proposes a joint gain and input design method for observer-based asymptotic active fault diagnosis, which is based on a newly-defined notion named the excluding degree of the origin from a zonotope. Using t...
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The microgrid (MG) system based on renewable energy generators plays a significant role in sustainable development and environmental protection, which has been developed rapidly. As a promising clean energy conversion...
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The microgrid (MG) system based on renewable energy generators plays a significant role in sustainable development and environmental protection, which has been developed rapidly. As a promising clean energy conversion technology, solid oxide fuel cell (SOFC) is a clean, efficient and controllable power generator, which is very suitable to be integrated in a distributed MG system. Finding the optimal size configuration and the beneficial operation strategy of the Grid-connected MG under different operation modes are critical issues for MG application. In this work, a single-dwelling MG incorporating solar photovoltaic (PV), wind turbine generator (WTG), SOFC and battery energy storage system (BESS) is studied by minimizing the system levelized cost of energy (LCOE) on the basis of system multi-constraints. The dispatching problem of MG is modeled as a quadratic programming problem and an improved GA-PSO algorithm is employed to explore the optimal configuration. Then, sensitivity analysis is carried out to identify the impact of different distributed energy resource size on the performance of MG. Based on the optimal configuration, the operation strategy of the proposed MG under both off-grid mode and grid-connected mode, as well as the influence of electricity price and fuel price on the operation of the MG are investigated. Finally, the economic and environmental benefits of the MG are studied and compared. The results show that, in Shanghai, Beijing, Wuhan and Hulunbuir, the costs of the MG in both off-grid mode and grid-connected mode are lower than the grid-supplied price by up to 13%-28% and 28%-37% separately.
Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carri...
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ISBN:
(纸本)9781728188089
Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carried on resource-constrained devices like mobile phones, given factor that such a deployment normally requires cost-effective considerations. In this work, we address this issue by presenting a novel Distilled Learning Framework(DLF) for siamese tracking, which aims at learning tracking model with efficiency and high accuracy. Specifically, we propose two simple yet effective knowledge distillation strategies, denote as point-wise distillation and pair-wise distillation, which are designed for transferring knowledge from a more discriminative teacher tracker into a compact student tracker. In this way, cost-effective and high performance tracking could be achieved. Extensive experiments on several tracking benchmarks demonstrate the effectiveness of our proposed method.
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