Lithium‐ion batteries (LIBs) have shown considerable promise as an energy storage system due to their high conversion efficiency, size options (from coin cell to grid storage), and free of gaseous ...exhaust. For LIBs, power density and energy density are two of the most important parameters for their practical use, and the power density is the key factor for applications such as fast‐charging electric vehicles, high‐power portable tools, and power grid stabilization. A high rate of performance is also required for devices that store electrical energy from seasonal or irregular energy sources, such as wind energy and wave energy. Significant efforts have been made over the last several years to improve the power density of LIBs through anodes, cathodes, and electrolytes, and much progress has been made. To provide a comprehensive picture of these recent achievements, this review discusses the progress made in high‐power LIBs from 2013 to the present, including general and fundamental principles of high‐power LIBs, challenges facing LIB development today, and an outlook for future LIB development.
Power density is one of the important parameters for lithium‐ion batteries (LIBs) in their practical applications. The progress in high‐power LIBs since 2013 has been reviewed, from fundamental principles to experimental practice. Challenges and outlook for high‐power LIB development have been highlighted.
Speech Dialogue System is currently widely used in various fields. Users can interact and communicate with the system through natural language. While in practical situations, there exist third-person ...background sounds and background noise interference in real dialogue scenes. This issue seriously damages the intelligibility of the speech signal and decreases speech recognition performance. To tackle this, in this paper, we exploit a speech separation method that can help us to separate target speech from complex multi-person speech. We propose a multi-task-attention mechanism, and we select TFCN as our audio feature extraction module. Based on the multi-task method, we use SI-SDR and cross-entropy speaker classification loss function for joint training, and then we use the attention mechanism to further excludes the background vocals in the mixed speech. We not only test our result in Distortion indicators SI-SDR and SDR, but also test with a speech recognition system. To train our model and demonstrate its effectiveness, we build a background vocal removal data set based on a common data set. Experimental results empirically show that our model significantly improves the performance of speech separation model.
•The local similarity pyramid module effectively captures multiscale features of infrared small targets.•The feature aggregation module considers to merge shallow and deep features with ...attention.•The proposed network outperforms other state-of-the-art methods.•The ablation study demonstrates the contribution of each module of proposed network.
Small target segmentation is one of the vital techniques in various infrared-based applications. The typical challenges are summarized as follows: the sizes of infrared small target are extremely small compared with common targets, and infrared small targets with dim appearances are similar to the background noise. To address the above problem, this paper studies how to leverage the powerful pyramid structure and attention mechanism for the segmentation of infrared small targets. Multiple well-designed local similarity pyramid modules (LSPMs) are endowed with a strong capability to model the multiscale features of infrared small targets. Specifically, each LSPM with a different scale estimates the weight of the local similarity, which quantifies the degree to which a pixel is similar to other pixels. The pyramid features are introduced into the feature aggregation module as the supplement of the global features. The proposed network aggregates features with different weights that facilitate the fusion of shallow and deep features. We empirically evaluate the proposed network on public infrared small target segmentation datasets. The experimental results demonstrate that the network achieves better performance than other state-of-the-art methods. The code is publicly available at https://github.com/HuangLian126/LSPM.
Zero-shot learning (ZSL) aims to classify instances whose classes could be unseen during training. Most existing ZSL methods project visual or semantic features into the space of the other one, or ...into a common subspace. The main goal of projection is to find out the similar features in the latent subspace. However, existing methods barely consider common features that preserve knowledge, here we refer to these features as the shared concepts, which are essential to model the relationship between the visual and semantic spaces. In this paper, we exploit the underlying concepts shared by both visual and semantic features in a latent common subspace and propose to match their latent visual and semantic representations. To reduce domain shift and information loss, we introduce reconstruction losses for both visual and semantic features. As a result, the reconstruction regularizations are added to the similar features and thereby obtain knowledge preserving shared concepts via the proposed method. Mathematically, it is formulated as the minimization problem for mutual orthogonal projection to their latent common subspace. The problem involves two projection variables, thus we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems in the scheme. These two subproblems are further solved by searching algorithms based on the Barzilai–Borwein stepsize. Extensive experiments on six benchmark data sets are conducted to demonstrate that the accuracy of the proposed method is better than that of existing ZSL methods.
•Match the latent representations in a common subspace for concept sharing.•The orthogonal constraints can avoid degeneracy and remove redundant information.•The resulting model is solved effectively by a Gauss–Seidel optimization scheme.•Results on six benchmark data sets validate the effectiveness of the proposed method.
The speech dialogue system has gradually been widely used in daily life. Users can consult and communicate with the system through natural language. However, in practical applications, third-person ...background sounds and background noise interference in real dialogue scenes will be encountered. The uncertainty and complexity of these background sounds will have a bad impact on the recognition of the system. A good speech enhancement module can help us to separate the target speaker from the original speech. Recently, a solution called SpEx+ was proposed from the time domain, but SpEx+ needs a reference speech to assist in training. This reference speech may have noise in actual applications that will affect performance. Therefore, we propose a Denoi-SpEx+ model. Before the reference speech is input to the network, a speech denoising network is added, so that the quality of speech separation in practical applications can be guaranteed. Experiments show that our model can significantly improve the performance of speech separation model of noisy reference speech.
With the popularity of unlocking and payment of using the face-swiping function, the era of face recognition has arrived. However, the current face recognition technology can identify the identity of ...the face image but cannot accurately distinguish the authenticity of the input face on embedded devices. Recently, face recognition with face presentation attack detection has become a research hotspot. In this paper, we develop an intelligent face recognition system on Raspberry Pi to handle this issue. Specifically, we first add a face presentation attack detection process to form a three-stage face recognition method to realize face recognition with face anti-spoofing. Second, we select a face detection model, a face presentation attack detection model and a face recognition model suitable for Raspberry Pi, and transplant these models previously trained in the Python environment to the C++ environment. Then we integrate and deploy these three models on Raspberry Pi and use the NCNN inference framework for efficient reasoning within limited memory and computing resources. Finally, we implement the face recognition system and conduct real-world tests to verify the performance. The experimental results demonstrate that our system can be applied in practice, and can achieve good effects.
Pd−Ag bimetallic dendrites have been synthesized via a galvanic replacement reaction of Ag dendrites in a Na2PdCl4 solution. Scanning and transmission electron microscopy (SEM and TEM), energy ...dispersive X-ray spectrometry (EDX), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) analysis reveal that the resulting product is composed of partially depleted Ag dendrites covered with a rough surface with many Pd granules protruding by up to about 20 nm. High-solution TEM combined with EDX and selected area electron diffraction (SAED) confirms the formation of bimetallic interfaces between Pd and Ag. These Pd−Ag dendrites show up to four times higher catalytic activity toward the reduction of 4-nitrophenol (4-NP) by sodium borohydride (NaBH4) than the best recently reported catalysts. This further enhancement over the already strong performance of similarly synthesized Au−Ag dendrites is explained by the presence of Pd, adding a hydrogen relay mechanism on top of the very effective electron relay capability of bimetallic dendrites.
•Graphene nanoplate added zinc-rich epoxy composite coatings were prepared.•The sacrificial anode-based corrosion protection is attributed to the Zn–GNP–Zn network.•The barrier effect of the coating ...on water uptake is enhanced by the addition of graphene nanoplates.•The delamination resistance of the coating is increased by graphene nanoplate addition.
In this work, graphene nanoplate (GNP) added zinc-rich epoxy (ZRE) composite coatings were prepared. Electrochemical measurements, surface characterization and galvanic corrosion testing were conducted to investigate the GNP-enhanced zinc (Zn) sacrificial anode-based corrosion protection of the coatings. The dispersed GNP serves as electronic channels to connect Zn particles to form a homogeneous Zn–GNP–Zn network in the coating matrix, resulting in an enhanced Zn sacrificial anode-based corrosion protection. The role of GNP in improved coating performance depends on Zn content in the coating. The prepared GNP-ZRE composite coatings also possess an enhanced delamination resistance.
Dendritic Ag/Au bimetallic nanostructures have been synthesized via a galvanic replacement reaction (GRR) of Ag dendrites in a chlorauric acid (HAuCl4) solution. After short periods of time, one ...obtains structures with protruding flakes; these will mature into very porous structures with little Ag left over. The morphological, compositional, and crystal structural changes involved with reaction time t were analyzed by using scanning and transmission electron microscopy (SEM and TEM, respectively), energy-dispersive X-ray spectrometry (EDX), and X-ray diffraction. High-resolution TEM combined with EDX and selected area electron diffraction confirmed the replacement of Ag with Au. A proposed formation mechanism of the original Ag dendrites developing pores while growing Au flakes cover this underlying structure at longer reaction times is confirmed by exploiting surface-enhanced Raman scattering (SERS). Catalytic reduction of 4-nitrophenol (4-NP) by sodium borohydride (NaBH4) is strongly enhanced, implying promising applications in catalysis.
High entropy alloy (HEA) nanoparticles have wide application prospects in electrocatalysis due to their unique structure. However, the surface of HEA nanoparticles has strong chemical activity due to ...numerous suspended and unsaturated bonds, which poses serious challenges to storage and transportation. To solve this problem, we used a double pulse carbothermal shock process to coat the surface of HEA nanoparticles with graphene. The formation mechanism of HEA nanoparticles and the graphene coating mechanism were discussed. The results showed that the HEA thin films were curled into nanoparticles by the change of orientation of twin structure under the thermal stress of carbothermal shock. After the complete HEA nanoparticles were formed, the carbon dissolved in the HEA nanoparticles will gradually precipitate out during a second carbothermal shock and form multiple layers of graphene on the surface, and forming a structure of graphene-coated HEA nanoparticles.
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