Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest i...
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The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevan...
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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c...
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1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the complex interactions between multiple individuals.
Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical im...
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With the development of smart grid and renewable energy technologies, residential load forecasting has become an increasingly important task. Short-term residential load forecasting is not only conducive to power disp...
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With the development of smart grid and renewable energy technologies, residential load forecasting has become an increasingly important task. Short-term residential load forecasting is not only conducive to power dispatching and peak-shaving and valley filling of the grid, but also good for residents to obtain higher economic benefit from renewable energy. This paper proposes a new baseline-refinement forecasting framework consisting of two main steps, baseline profile construction and refinement predictions. Firstly, a baseline profile construction method is proposed to forecast the baseline load profile based on the similarity and cyclic patterns of daily load profiles. Secondly, for the refinement predictions, a bi-attention mechanism is proposed by combining self-attention mechanism and external attention mechanism to fulfill the feature transformation and is included in the temporal convolutional network to refine the baseline profile. The final load forecasting results are obtained by aggregating the baseline profile and the refinement predictions. The simulation results demonstrate that the proposed framework has smaller forecasting errors and higher forecasting stability than the commonly used models on two load forecasting metrics.
In industrial production processes, defect inspection plays an important role in reducing the occurrence of failures and improving production efficiency. Data-driven algorithms represented by deep learning have made g...
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This article mainly considers the output synchronization (OS) problem of multiple weighted and adaptive output coupled reaction-diffusion neural networks (RDNNs) without and with coupling delays in finite time. Withou...
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This article mainly considers the output synchronization (OS) problem of multiple weighted and adaptive output coupled reaction-diffusion neural networks (RDNNs) without and with coupling delays in finite time. Without coupling delays, an adaptive control law and an output feedback controller are, respectively, proposed to ensure that the multiple weighted and output coupled RDNNs are output synchronized and $H_{infinity}$ output synchronized in finite time. With coupling delays, an adaptive coupling weights control scheme and a novel feedback controller are put forward to make the multiple weighted RDNNs with output couplings achieve OS in finite time. Moreover, the finite-time $H_{infinity}$ OS is considered in the presence of external disturbances. By the Lyapunov approach, several finite-time OS and $H_{infinity}$ OS criteria are given. Finally, two simulation examples are presented to justify the effectiveness of the proposed adaptive control laws and controllers.
Robotic exoskeletons, which assist in stand-to-kneel and kneel-to-stand (STK-KTS) movements and static kneeling postures are in great demand in the nursing field. This movement involves continuous adjustment of the ce...
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Robotic exoskeletons, which assist in stand-to-kneel and kneel-to-stand (STK-KTS) movements and static kneeling postures are in great demand in the nursing field. This movement involves continuous adjustment of the center of gravity without a sufficient support polygon, which enhances the required joint effort of the ankle and knee. This study proposes a novel passive lower limb exoskeleton to support the movement. The exoskeleton was attached to the right leg and comprised a gas spring. The design followed an assistive strategy of the expanded support polygon. During the STK-KTS, the gas spring provided extra contact with the ground, thereby expanding the support polygon to increase motion stability and propping the knee to provide torque to the leg. The effectiveness of the gas spring was analyzed using a Lagrange dynamics-based simulation. Moreover, it was confirmed that the support polygon was expanded due to the proposed exoskeleton in real-world experiments. Further, experiments with seven healthy subjects showed that the exoskeleton reduced the time-integrated myoelectric potentials of the legs during STK-KTS (13.6%) and static posture (37.9%). These results imply that the proposed exoskeleton has the potential to reduce physical loads and provide a comfortable working environment for nursing workers.
In rehabilitation practice, motivating patients with neurological injuries to actively increase muscle activity and ensure their safety are important. Therefore, this study proposed a position-constrained assist-as-ne...
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In rehabilitation practice, motivating patients with neurological injuries to actively increase muscle activity and ensure their safety are important. Therefore, this study proposed a position-constrained assist-as-needed (AAN) control method for rehabilitation robots. A human-robot interaction system with position constraints was first established based on prescribed performance. Aiming at implementing the AAN strategy, the robot assistance level metric (RALM), a constructed global continuous differentiable function incorporating dead zone and saturation characteristics, was introduced to quantify the robotic assistance and facilitate seamless operation. To bridge the gap between the position constraints and the AAN strategy, a sliding manifold was constructed for the constrained human-robot dynamic system, where RALM was regarded as a weight factor to achieve a human-dominated mode, a robot-dominated mode, and their smooth transition, regarded as a human-robot shared mode. The stability of the closed-loop system was guaranteed by using the Lyapunov theory, and the proposed controller was verified by several physical experiments on a knee exoskeleton driven by pneumatic muscles.
Incremental learning models need to update the categories and their conceptual understanding over time. The current research has placed more emphasis on learning new categories, while another common but under-explored...
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Incremental learning models need to update the categories and their conceptual understanding over time. The current research has placed more emphasis on learning new categories, while another common but under-explored incremental scenario is the updating and refinement of category labels. In this paper, we present the Hierarchical Task-Incremental Learning (HTIL) problem, which emulates the human cognitive process of progressing from coarse to fine. While the model learns the fine categories, it gains a better understanding of the perception of coarse categories, thereby enhancing its ability to differentiate between previously encountered classes. Inspired by neural collapse, we propose to initialize the coarse class prototypes and update the new fine class using hierarchical relations. We conduct experiments on diverse hierarchical data benchmarks, and the experiment results show our method achieves excellent results.
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