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In this article, a new morphing method is proposed for two linear features at different scales, based on their entire structures ( MLBES in abbreviation). First, the bend structures of the linear features are identified by using a constrained Delaunay triangulation ( CDT in abbreviation) model and represented by binary bend-structure trees. By matching the independent bends represented by the bend-structure trees, corresponding independent bends are obtained. These corresponding independent bends are further used to match their child bends based on hierarchical bend structures so that corresponding bends are obtained. On this basis, the two linear features are split into pairs of corresponding subpolylines by the start and end points of the corresponding bends. Second, structures of the corresponding subpolylines are identified by the Douglas- Peucker algorithm and represented by binary line generalization trees ( BLG-trees in abbreviation). The corresponding subpolylines are split into smaller corresponding subpolylines by matching the nodes of the BLG-trees. Third, the corresponding points are identified by using the linear interpolation algorithm for every pair of corresponding subpolylines. Finally, straight-line trajectories are employed to generate a family of intermediate-scale linear features. By comparison with other methods, it is found that MLBES is accurate and efficient. [ABSTRACT FROM AUTHOR]

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit. [ABSTRACT FROM AUTHOR]

The modeling of channel and temporal information is of crucial importance for action recognition tasks. To build a high-performance action recognition network by effectively capturing channel and temporal information, we propose CLS-Net: an action recognition algorithm based on channel-temporal information modeling. The proposed CLS-Net characterizes channel and temporal information by inserting multiple modules to an end-to-end backbone network, including a channel attention module (CA module) for modeling channel information, a long-term temporal module (LT module) and a short-term temporal module (ST module) for modeling temporal information. Specifically, the CA module extracts the correlation between feature channels so the network can learn to selectively strengthen the features containing useful information and suppress the useless features through global information. The LT module moves some channels in the temporal dimension to realize information interaction across time domains and model global temporal information. The ST module enhances the motion-sensitive features by calculating the feature-level frame difference information and realizes the representation of local motion information. Since the multi-module insertion mode directly affects the whole model's final performance, we propose a novel multi-module insertion mode instead of a simple series or parallel connection to ensure that the multiple modules can complement one another and cooperate with each other more efficiently. CLS-Net achieves SOTA performance on the EgoGesture and Jester dataset in the same type of network and achieves competitive results on the Something-Something V2 dataset. [ABSTRACT FROM AUTHOR]

Sodium metal batteries (NMBs) have attracted increasing attention as next‐generation rechargeable batteries. How to improve their cycling stability and safety under limited sodium excess conditions, ideally zero sodium excess (i.e., anode‐free architecture), is highly desired yet remains challenging. Herein, it is demonstrated that sodium formate (HCOONa), one component of the solid electrolyte interphase (SEI) naturally formed on sodium metal anode, is a promising candidate for designing high‐performance artificial SEI layers, which can suppress the sodium dendrite formation and reduce the side reactions between sodium and the electrolyte. Profiting from the HCOONa interface, the Na|Na3V2(PO4)3 battery with a high mass loading of Na3V2(PO4)3 (10 mg cm−2) exhibits a superior cycling stability with an ultralow decay rate of 0.004% per cycle over 800 cycles. More impressively, a single molecular layer of HCOONa in situ formed on commercial copper current collector helps to extend the lifespan of the anode‐free Cu|Na3V2(PO4)3 battery to 400 cycles with 88.2% capacity relation, representing the longest cycle lifetime reported in anode‐free NMBs. [ABSTRACT FROM AUTHOR]

In order to address the problem with low signal-to-noise ratio (SNR) detection for airborne magnetic anomaly detection (MAD), an adaptive detection method based on bi-stable stochastic resonance (SR) system is proposed. The method adopts a series structure, S-SR, effectively improving detection speed. Allowing for the difficulty in parameter selection for the SR system, this method can achieve the adaptive selection of system parameters. On this basis, the effect of different sampling frequencies in the Runge–Kutta method on the numerical model of the SR system is investigated. Finally, the simulation and experiment analysis of MAD methods are performed to draw a comparison between the S-SR, orthogonal basis functions (OBF) decomposition, and high-order crossing (HOC) methods. According to the simulation and experiment results, the S-SR method outperforms the OBF and HOC methods in the outcome and range of detection under the same conditions. [Display omitted] • A magnetic anomaly detection method based on bi-stable stochastic resonance system. • A magnetic anomaly signal model of the underwater target. • An adaptive selection method of stochastic resonance system parameters. • The optimization of the numerical model of stochastic resonance system. [ABSTRACT FROM AUTHOR]

A series of soft magnetic nanogranular FeCo–Ti–O films with different Ti contents were fabricated on Si substrates by co-sputtering. By adjusting the Ti content, it appears that the imaginary part of the permeability spectra of FeCo–Ti–O films changes from a single peak to double peaks then to a single peak. Frequency conversion ferromagnetic resonance (FMR) analysis evidences that double FMR peaks can be found in all three films, which were attributed to local anisotropy or two magnetic phases embedded in amorphous TiO x matrix. Structural and temperature dependent resistivity analysis support such three component microstructure. The overlapping double FMR peaks provide a wide absorption frequency band over 3 GHz, which is critical for the design of electromagnetic interference suppressor. [ABSTRACT FROM AUTHOR]

The phase equilibria of Co–V binary system were experimentally investigated, and the magnetically induced phase separation in the fcc phase was observed in a Co/V diffusion couple. Based on previous research and present work, a thermodynamic reassessment in the Co–V binary system was carried out by means of the CALPHAD method. The calculated results are consistent with the experimental data. The metastable miscibility gap of the hcp phase in the Co–V binary system was thermodynamically calculated. According to the calculation, the Co–V thin films with three concentrations were designed and deposited by magnetron sputtering. The transmission electron microscope (TEM) results prove that the metastable phase separation in the hcp phase exists in the Co–V system. The determined magnetic properties of the thin films reveal that the values of coercivity and remanence ratio are function of V content. [ABSTRACT FROM AUTHOR]

Abstract: The Co3O4/AAO composite with novel morphology was successfully prepared by the solution-dipping method and the calcination of cobalt acetate precursor at 400°C for 2h in air. Porous anodic alumina (AAO) thin film was selected as new substrate to support colloidal monolayer template. Scanning electron microscopy (SEM) analysis indicated that the morphology of the as-prepared product could be controlled by changing the concentration of precursor. Catalytic test shows that the obtained Co3O4/AAO composite has a high decolorization ratio in the degradation of methylene blue (MB), which reaches 97.79% in 140min in the presence of sodium borohydride (NaBH4). Comparative experiments show that this nanomaterial has enhanced catalytic performance. [Copyright &y& Elsevier]

This paper investigates a novel distributed fusion system for target tracking in three dimensional surveillance using multiple asynchronous BO sensors by simultaneously addressing the problems of target mis-detection and clutter disturbance as well as the out-of-sequence (OOS) information fusion. The proposed distributed fusion architecture consists of the local tracking and distributed fusion, wherein, the refined BO measurements output from the local tracking in each BO sensor are transmitted to the fusion center for subsequent distributed fusion. In the local tracking, instead of kinematic state, the target pseudo state is defined and tracked by operating the proposed pseudo state-integrated probabilistic data association (PS-IPDA) algorithm with an approximately linear pseudo state evolution model, resulting in greatly refined BO sensor measurements. In the fusion center, the sequential asynchronous IPDA-general one-step retrodiction update (SAIPDA-GORU) algorithm is proposed to effectively fuse the transmitted origin-unknown OOS refined measurements (OOSRMs) with the central tracks. The SAIPDA-GORU propagates the central track hybrid state to the OOS time by deploying a general one-step retrodiction technique, and mathematically formulates the OOSRMs-to-retrodicted central track association probabilities utilized to facilitate the fusing of currently filtered central track with OOSRMs. Numerical results show that the proposed PS-IPDA algorithm reduces the errors of target bearings measurement by around a half and eliminates more than 60% of the clutter measurements contained in raw detections, saving significant communication bandwidth between local sensors and fusion center. Furthermore, the proposed SAIPDA-GORU method tangibly improves the estimation accuracy and false track discrimination over the existing discarding method, with the potential to be implemented in real-time. [ABSTRACT FROM AUTHOR]

The grain size and the ferroelectric morphology are the two most important factors in the research and development of advanced dielectric devices. It is important to synthesize nanostructures in order to achieve optimal grain size and inhibit grain growth while controlling the sintering parameters. It is also important to find new methods to study the grain growth during densification. BaTiO3 films were successfully obtained via the sol-gel spin coating method. In this paper, we report the deposition of nanostructured BaTiO3 thin film by spin coating of sol-gel precursors. The densification, grain growth and microstructure evolution have been studied. Moreover, a novel physical and mathematical fractal analysis have been applied successfully to the densification and nano grain growth of BaTiO3 thin film. The discrepancy between the fractal dimension and the grain growth with increasing temperatures and soaking time are presented and discussed. Image 1062 • Analisy BaTiO3 nanograin growth with the sintering parameters influence on kinetics. • All of these sintering analysis we are enriching with fractal nature approach. • New approach enlight consolidation and morphology by fractal nature analysis. • The fractal dimension characterisation is included to complete analysis. • Here, we introduce the fractal nature correction influence on sintering temperature. [ABSTRACT FROM AUTHOR]

To address the problem that the performance of the detector in airborne magnetic anomaly detection (MAD) is terrible, a stochastic resonance (SR) detection algorithm based on orthonormalized basis function (OBF-SR) is proposed for MAD under low signal-to-noise ratio conditions. The signal contaminated by noise is first preprocessed by the OBF method, where the sum of the three components in the OBF space is selected as the SR system input. Then, a parallel SR system with different initial states is designed to detect the signal. Finally, the simulation analysis of MAD methods is performed to draw a comparison between the OBF-SR method, the typical SR method, and the OBF method. The results show that the OBF-SR method outperforms the SR and OBF methods in the detection probability and detection range under the same conditions., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts., (© Springer Science+Business Media, LLC, part of Springer Nature 2020.)