Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigatio...
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Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy h...
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Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) ...
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Semantic Segmentation is the foundation of scene understanding and automatic driving tasks. One of the challenges of semantic segmentation is the reduction of feature resolution as the network goes deep. In this paper...
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Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC) technology, MFCS can provide higher system power and longer service life. As the number of stacks in...
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For large-scale heterogeneous multi-agent systems (MASs) with characteristics of dense-sparse mixed distribution, this paper investigates the practical finite-time deployment problem by establishing a novel cross-spec...
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For large-scale heterogeneous multi-agent systems (MASs) with characteristics of dense-sparse mixed distribution, this paper investigates the practical finite-time deployment problem by establishing a novel cross-species bionic analytical framework based on the partial differential equation-ordinary differential equation (PDE-ODE) approach. Specifically, by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents, this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model, and that of sparsely distributed agents as several ODEs equivalent to the predator population models. The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE, thereby forming a PDE-ODE-based tracking error model for the considered MASs. Furthermore, by integrating adaptive neural control scheme with the aforementioned biological models, a “Flexible Neural Network” endowed with adaptive and self-stabilized capabilities is constructed, which acts upon the considered MASs, enabling their practical finite-time deployment. Finally, effectiveness of the developed approach is illustrated through a numerical example.
In today’s ever-changing world, the ability of machine learning models to continually learn new data without forgetting previous knowledge is of utmost importance. However, in the scenario of few-shot class-increment...
In today’s ever-changing world, the ability of machine learning models to continually learn new data without forgetting previous knowledge is of utmost importance. However, in the scenario of few-shot class-incremental learning (FSCIL), where models have limited access to new instances, this task becomes even more challenging. Current methods use prototypes as a replacement for classifiers, where the cosine similarity of instances to these prototypes is used for prediction. However, we have identified that the embedding space created by using the relu activation function is incomplete and crowded for future classes. To address this issue, we propose the Expanding Hyperspherical Space (EHS) method for FSCIL. In EHS, we utilize an odd-symmetric activation function to ensure the completeness and symmetry of embedding space. Additionally, we specify a region for base classes and reserve space for unseen future classes, which increases the distance between class distributions. Pseudo instances are also used to enable the model to anticipate possible upcoming samples. During inference, we provide rectification to the confidence to prevent bias towards base classes. We conducted experiments on benchmark datasets such as CIFAR100 and miniimageNet, which demonstrate that our proposed method achieves state-of-the-art performance.
Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cel...
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Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel *** from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of *** response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is ***,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.
Sensing 3D objects is critical when 2D object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3D objects after deploying it. Therefore, the robot shou...
Sensing 3D objects is critical when 2D object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3D objects after deploying it. Therefore, the robot should be able to learn continuously in real-world scenarios. Few-shot class-incremental learning (FSCIL) requires the model to learn from few-shot new examples continually and not forget past classes. However, there is an implicit but strong assumption in the FSCIL that the distribution of the base and incremental classes is the same. In this paper, we focus on cross-domain FSCIL for point-cloud recognition. We decompose the catastrophic forgetting into base class forgetting and incremental class forgetting and alleviate them separately. We utilize the base model to discriminate base samples and new samples by treating base samples as in-distribution samples, and new objects as out-of-distribution samples. We retain the base model to avoid catastrophic forgetting of base classes and train an extra domain-specific module for all new samples to adapt to new classes. At inference, we first discriminate whether the sample belongs to the base class or the new class. Once classified at the model level, test samples are then passed to the corresponding model for class-level classification. To better mitigate the forgetting of new classes, we adopt the soft label and hard label replay together. Extensive experiments on synthetic-to-real incremental 3D datasets show that our proposed method can balance the performance between the base and new objects and outperforms the previous state-of-the-art methods.
RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full...
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