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Conversion-type transition-metal phosphide anode materials with high theoretical capacity usually suffer from low-rate capability and severe capacity decay, which are mainly caused by their inferior electronic conductivities and large volumetric variations together with the poor reversibility of discharge product (Li3P), impeding their practical applications. Herein, guided by density functional theory calculations, these obstacles are simultaneously mitigated by confining amorphous FeP nanoparticles into ultrathin 3D interconnected P-doped porous carbon nanosheets (denoted as FeP@CNs) via a facile approach, forming an intriguing 3D flake-CNs-like configuration. As an anode for lithium-ion batteries (LIBs), the resulting FeP@CNs electrode not only reaches a high reversible capacity (837 mA h g-1 after 300 cycles at 0.2 A g-1) and an exceptional rate capability (403 mA h g-1 at 16 A g-1) but also exhibits extraordinary durability (2500 cycles, 563 mA h g-1 at 4 A g-1, 98% capacity retention). By combining DFT calculations, in situ transmission electron microscopy, and a suite of ex situ microscopic and spectroscopic techniques, we show that the superior performances of FeP@CNs anode originate from its prominent structural and compositional merits, which render fast electron/ion-transport kinetics and abundant active sites (amorphous FeP nanoparticles and structural defects in P-doped CNs) for charge storage, promote the reversibility of conversion reactions, and buffer the volume variations while preventing pulverization/aggregation of FeP during cycling, thus enabling a high rate and highly durable lithium storage. Furthermore, a full cell composed of the prelithiated FeP@CNs anode and commercial LiFePO4 cathode exhibits impressive rate performance while maintaining superior cycling stability. This work fundamentally and experimentally presents a facile and effective structural engineering strategy for markedly improving the performance of conversion-type anodes for advanced LIBs.

Active crystal facets can generate special properties for various applications. Herein, we report a (001) faceted nanosheet-constructed hierarchically porous TiO2/rGO hybrid architecture with unprecedented and highly stable lithium storage performance. Density functional theory calculations show that the (001) faceted TiO2 nanosheets enable enhanced reaction kinetics by reinforcing their contact with the electrolyte and shortening the path length of Li+ diffusion and insertion-extraction. The reduced graphene oxide (rGO) nanosheets in this TiO2/rGO hybrid largely improve charge transport, while the porous hierarchy at different length scales favors continuous electrolyte permeation and accommodates volume change. This hierarchically porous TiO2/rGO hybrid anode material demonstrates an excellent reversible capacity of 250 mAh g-1 at 1 C (1 C = 335 mA g-1) at a voltage window of 1.0-3.0 V. Even after 1000 cycles at 5 C and 500 cycles at 10 C, the anode retains exceptional and stable capacities of 176 and 160 mAh g-1, respectively. Moreover, the formed Li2Ti2O4 nanodots facilitate reversed Li+ insertion-extraction during the cycling process. The above results indicate the best performance of TiO2-based materials as anodes for lithium-ion batteries reported in the literature.

Additional file 1: Fig. S1 SAED patterns of Mo5Ni1 (a), Mo2Ni1 (b) and Mo1Ni1 (c) samples. The indexes in white correspond to Ni3Se4 while those of red correspond to MoSe2. Fig. S2 SEM image (a) and TEM image (b) of pure MoSe2. Fig. S3 TEM images of Mo2Ni1 sample obtained using different injection rates. (a) 3.3 mL/min. (b) 1.65 mL/min. (c) XRD patterns (The bottom pattern corresponds to an injection rate of 1.65 mL/min while the up one to 3.3 mL/min). Fig. S4 Cyclic voltammetry curves of pure (a) MoSe2, (b) Mo5Ni1, (c) Mo2Ni1, (d) Mo1Ni1 and (e) pure Ni3Se4 in the region of 0.1 ~ 0.2 V vs RHE. Fig. S5 Cyclic voltammograms (-0.1~0.6 V vs RHE) recorded in pH = 7 phosphate buffer.

The basic idea of multi-class classification is a disassembly method, which is to decompose a multi-class classification task into several binary classification tasks. In order to improve the accuracy of multi-class classification in the case of insufficient samples, this paper proposes a multi-class classification method combining K-means and multi-task relationship learning (MTRL). The method first uses the split method of One vs. Rest to disassemble the multi-class classification task into binary classification tasks. K-means is used to down sample the dataset of each task, which can prevent over-fitting of the model while reducing training costs. Finally, the sampled dataset is applied to the MTRL, and multiple binary classifiers are trained together. With the help of MTRL, this method can utilize the inter-task association to train the model, and achieve the purpose of improving the classification accuracy of each binary classifier. The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset, Wine dataset, Multiple Features dataset, Wireless Indoor Localization dataset and Avila dataset.

This paper considers the decline problem of tracking accuracy caused by acceleration estimation inaccuracy in hybrid grid multiple model (HGMM) algorithm. A new improved tracking algorithm of maneuvering targets based on CSHGMM is proposed. Grid partition is performed by estimation of the acceleration mean in current statistic (CS) model, where a fading factor is adopted to improve tracking accuracy by adjusting the gain matrix under the condition of the abrupt change of acceleration. Then possible model sets of the target are generated online, and interactive multiple model algorithm is adopted to estimate the target state. The proposed algorithm performance is tested and compared in general and strong maneuvering scenarios, and simulation results demonstrate that the proposed algorithm not only improves the accuracy for maneuvering target tracking, but also can be applied to most of the tracking scenarios.

Aiming at the problem of more than one measurement in a tracking gate, this paper introduces the concept of true trajectory probability based on the using of tracks splitting method, therefore the process of target tracking falls into two parts: states update and true trajectory probability update. True trajectory probability denotes the possibility of the trajectory being the track of the actual target. The updated values of the true trajectory probability depend on the prior information of measurements in current time. The method also obviates the need for track initiation and track termination. Proper assignment and dynamic adjusting of the probability gate make the real-time tracking more effective as well as reducing the consumption of the system resources.

This paper proposes a novel sequential asynchronous fusion algorithm by using the idea of sequential discretization of the sampling points based on a continuous and distributed multi-sensor linear dynamic system. Firstly, it maps and unifies all measurements in the reference frame and clock with fusion centre. Secondly, selecting every sampling time in the fusion period to discretize the continuous state system sequentially, we get the state equation and the relevant measurement equation between every sampling point in this period. Finally, using the best linear Kalman filter in the sense of LMMSE directly, the sequential filtering fusion of asynchronous sampling measurements in this period can be realized. Compared with the existing typical asynchronous algorithms which depend on equivalent pseudo-measurements, the proposed algorithm has lower computational load, better real-time and accurateness. This paper elaborates the form of this new algorithm, and finally computer simulation demonstrates validity of the new algorithms.

For the multiple source information fusion in complex environment problem, An information representation model based on the uncertain basis and a robust fusion algorithm are presented. First, a definition of uncertain basis is given and a realization method with kernel function is proposed. Based on the uncertain basis, an uncertain information description model is presented. Second, an adaptive kernel bandwidth robust fusion technique is proposed. The kernel bandwidth is initially decided by sample covariance and then is adjusted after each iterative to describe the estimated uncertainty. Theory analysis and simulation show that, the proposed method equals to the existing techniques in good scenario and takes advantage in complex environment.

In this paper, a new fusion rule based on pulse coupled neural network (PCNN) and the clarity of images is proposed for multi-band synthetic aperture radar (SAR) images fusion. After the nonsubsampled Contourlet transform (NSCT) of the registered SAR images, we can get a flexible multiscale, multidirectional and shift-invariant representation of images. Then the linking strength of each neuron in PCNN is determined by the clarity of images, and new fire mapping images are obtained for each decomposed subband image taking part in the fusion. Experimental results show that the new fusion rule outperforms some classical rules, and after the inverse NSCT, the obtained fusion image can preserve much information of textures and edges of images than its counterparts.

Finding efficient geometric representations of images is a central issue in improving the efficiency of image compression. Bandelet provides an efficient way for image representation based on geometric regularity. However, a degeneration of performance will appear in high ratio compression for all the transformation based compression approaches. Moreover, Bandelet is time consuming and lacks of flexibility. In this paper, we construct a Bandelet network for image compression based on discrete Bandelet frame, in which the Bandelet basis is adopted as the activation function in the hidden layer of a feed-forward neural network. The Bandelet basis can provide an efficient representation of image. Moreover, neural networks based methods is more flexible and can achieve high compression ratio with its parallel implementation structure, when compared with transformation based image compression approaches. The construction and the leaning of the Bandelet network are addressed. Experiment results show that it can provide a potential way for SAR image compression.

The theory of fuzzy sets has been used to deal with image enhancement problems for degraded images in which the image edges are uncertain and inaccurate. For those kinds of images, to some extent, the good enhancement effect can be obtained using the fuzzy sets-based image enhancement method instead of the traditional image enhancement approaches. The gray level maximum has not been changed in the classical fuzzy enhancement method proposed by S. K. Pal, so this method is not fit for the enhancement problem of degraded images with less gray levels and low contrasts; the fact that the range of membership function of gray levels is not normalization form, i.e. [0,1], is another disadvantage of the traditional fuzzy enhancement approach. To deal with the problems mentioned above, a generalized iterative fuzzy enhancement algorithm is proposed in this paper. A new image quality assessment criterion is suggested on the basis of the statistical features of the gray-level histogram of images to control the iterative procedure of the proposed image enhancement algorithm. Computer simulation results showed that this new enhancement method is more suitable than fuzzy enhancement and gray-level transformation for handling the enhancement problems of images with less gray levels and low contrasts.

The present paper deals with dynamics modeling and controller designing for a large flexible spacecraft, such as tracking and data relay satellite (TDRS) with several flexible appendages and a liquid fuel tank. The basic parameters for the equivalent mechanical model under low-gravity are given when the pendulum motion of the tank is small enough such that it can be approximated linearly. Hybrid coordinate is used to describe the configuration of the large flexible liquid-filled spacecraft, and the D' Alembert principle in virtual work is employed to derive the dynamics equations. The equations obtained are simple and common, which can be applied to describe many complicated open-chain spacecraft systems. A controller is designed using the variable structure control strategy. The control mission consists of maneuvering the large antennas to track the pre-selected motion laws, stabilizing the spacecraft in an inertial space and suppressing the elastic vibration of the appendages. A numerical example is given to prove the validity of the control laws.

In the present work, a series of [Fe80Ni20—O/SiO2]n multilayer thin films is fabricated using a reactive magnetron sputtering equipment. The thickness of SiO2 interlayer is fixed at 3 nm, while the thickness values of Fe80Ni20—O magnetic films range from 10 nm to 30 nm. All films present obvious in-plane uniaxial magnetic anisotropy. With increasing the Fe80Ni20—O layer thickness, the saturation magnetization increases slightly and the coercivity becomes larger due to the enlarged grain size, which could weaken the soft magnetic property. The results of high frequency magnetic permeability characterization show that films with thin magnetic layer are more suitable for practical applications. When the thickness of Fe80Ni20—O layer is 10 nm, the multilayer film exhibits the most comprehensive high-frequency magnetic property with a real permeability of 300 in gigahertz range.