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As an important technology in artificial intelligence, Granular Computing has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of Granular Computing. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we elaborate on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the underlying conceptual framework. The results obtained on both synthetic and publicly available datasets are reported to show the enhancement of the data reconstruction performance thanks to the proposed method. It is pointed out that by using the proposed approach in some cases the reconstruction errors can be reduced close to zero by using the proposed approach.

In this article, an augmented subspace-based algorithm for detecting the direction of arrival (DOA) of signals is presented. In the developed scheme, a uniform circular antenna array with a full range of lateral capacity is first transformed into a uniform linear antenna array in which the steering matrix has a Vandermonde form. This kind of structure can be exploited to formulate computationally efficient search-free estimation methods. In the process of virtual transformation, a novel DOA unit matrix of an assumed sector where the signals to be detected are located is built and optimized to find response matrices of the real and virtual antenna arrays to signals from the sector. By utilizing singular value decomposition (SVD) of the response matrix of the real antenna array to possible signals from the sector, a stable virtual transformation matrix between the real and virtual antenna arrays is obtained. Meanwhile, the aperture of the original array is also expanded during this process. Then, a virtual cyclic optimization algorithm is introduced to thoroughly mine information from the correlation matrices of the virtual antenna array receiving data and to optimize the signal subspace. Subsequently, the DOA can be efficiently determined through the reconstruction of the steering matrix. In the experimental part of the study, root-mean-square errors and probability of success are used to evaluate the performance of the algorithms. In particular, simulation results demonstrate that the proposed methods provide significant accuracy improvements, especially for low signal-to-noise ratio thresholds.

Soybean protein isolate (SPI) shows a broad application prospect in the food and packaging industry. However, its inferior mechanical properties and water resistance limit its application. In this work, a series of SPI-based composite films were prepared by combining with cellulose nanofiber (CNF), graphene oxide (GO), GO/CNF, ethylene glycol diglycidyl ether (EDGE) or GO/CNF/EGDE. The results show that by adding a small amount of reinforced materials (3%), the water resistance, hydrophilicity, mechanical properties and thermal stability of composite films were improved. The filling effect and hydrogen bonding of the reinforcing materials contribute to the formation of film structure. EGDE cross-link SPI with CNF and GO build a chemical network to improve the properties of the film. In addition, they could make a synergistic effect to better enhance the performance of a protein film. Therefore, the tensile strength and elastic modulus of the SGCE film reached 469.21% and 367.58%, respectively.

The weak rate capability and cyclic stability are stubborn issues limiting the application of intriguing lithium-rich layered oxide in the lithium-ion batteries. In this work, a glucose-based treatment was proposed to modify the microstructure and electrochemical properties of the lithium-rich layered oxide. A newly formed layered-spinel surface and chemically activated Li2MnO3 component were acquired through the glucose-based procedure. Notably increased initial coulombic efficiencies, discharge capacities, rate performance, and cyclic stability were observed in the modified samples. Typically, the discharge capacity and central voltage retentions after 100 cycles grow from 73.4% and 78.7% for the pristine to 84.1% and 86.2% for the treated sample. This improvement in electrochemical properties of the treated sample results from the co-contribution of the significantly promoted redox reaction at cathode surface, accelerated Li+ bulk diffusion rate, and tuned electrochemical evolution behavior of the lithium-rich layered oxide. This work provides a green and cost-efficient way for promoting the electrochemical properties of the lithium-rich layered oxide.

Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgnal Classification (MUSIC) and Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability for detecting DOA, are two examples of an eigen-subspace algorithm. However, missed detection and estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) and snapshot deficiency (small time-domain samples of the observed signal), especially for sources with different SNRs. To avoid the above problems, in this study, we develop a DOA detection approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In the developed scheme, according to received data, a noise subspace is established through performing an eigen-decomposition operation on a sampling covariance matrix. Then, a collection of angles randomly selected from the observation space are used to build a potential signal subspace on the basis of the steering matrix of the array. Afterwards, making use of the fact that the signal space is orthogonal to the noise subspace, a cost function, which contains the desired DOA information, is designed. Thus, the problem of capturing the DOA information can be transformed into the optimization of the already constructed cost function. In this respect, the DOA finding of multiple signal sources—that is, the multi-objective optimization problem—can be regarded as a single objective optimization problem, which can effectively reduce the probability of missed detection of the signals. Subsequently, the QPSO is employed to determine an optimal signal subspace by minimizing the orthogonality error so as to obtain the DOA. Ultimately, the performance of DOA detection is improved. An explicit analysis and derivation of the developed scheme are provided. The results of computer simulation show that the proposed scheme has superior estimation performance when detecting signals with very different SNR levels and small snapshots.

The separation of signal-and-noise subspaces is a crucial step in many array signal processing applications, since the performance of most high-resolution methods mainly depends on the accuracy of separated signal-and-noise subspaces. If the resulting signal subspace is inaccurately estimated, high-resolution subspace-based algorithms would most likely fail. In this paper, we propose a high-accuracy method to complete the separation of signal-and-noise subspaces. In the developed scheme, eigenvalues of the covariance matrix of the data received by the uniform linear array are first employed to construct an ideal sample space using the Schur product of matrices. Then, the Gaussian kernel is introduced to map the sample space into a new high-dimensional feature space, where the resulting structure becomes linearly separable. In the sequel, we propose two fuzzy set-based methods to divide the feature space into two subspaces, which correspond to the signal-and-noise subspaces. In this way, the signal subspace becomes separated. Experimental results show that, compared with the results produced by five other commonly used algorithms, the proposed method yields much higher accuracy, especially for low signal-to-noise ratio thresholds and small snapshots.

Li-rich layered oxides are promising cathode materials for advanced Li-ion batteries because of their high specific capacity and operating potential. In this work, the Li-rich layered oxide Li 1·2 Mn 0·54 Ni 0·13 Co 0·13 O 2 (LMNC), is modified via a carbonization-reduction process (yielding the corresponding reduced compound denoted LMNC-R). Compared to the pristine oxide, LMNC-R delivers significantly enhanced initial discharge capacity/columbic efficiency, remarkably improved rate performance with an accelerated Li + diffusion rate, and significantly increased capacity/voltage retention. The specific energy density and energy retention after 100 cycles increase from 378.2 Wh kg −1 and 47.7% for LMNC to 572.0 Wh kg −1 and 71.3%, respectively, for LMNC-R. The enhancement in the electrochemical performance of LMNC-R can be attributed to the synchronous formation of the oxygen non-stoichiometric Li 2 MnO 3-δ component and to the carbon/spinel double coating layer in the material that resulted from the post-treatment process. Thus, the carbonization-reduction modification process can be used to tailor the structural evolution procedure and to suppress the metal ion dissolution of the Li-rich layered oxide during cycling.

Information granules have been considered as the fundamental constructs of Granular Computing. As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. The FCM-based granulation–degranulation mechanism plays a pivotal role in Granular Computing. In this paper, to enhance the quality of the degranulation (reconstruction) process, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors (fuzzification factor vector) and setting up an adjustment mechanism to modify the prototypes and the partition matrix. The design is regarded as an optimization problem, which is guided by a reconstruction criterion. In the proposed scheme, the initial partition matrix and prototypes are generated by the FCM. Then a fuzzification factor vector is introduced to form an appropriate fuzzification factor for each cluster to build up an adjustment scheme of modifying the prototypes and the partition matrix. With the supervised learning mode of the granulation–degranulation​ process, we construct a composite objective function of the fuzzification factor vector, the prototypes and the partition matrix. Subsequently, the particle swarm optimization is employed to optimize the fuzzification factor vector to refine the prototypes and develop the optimal partition matrix. Finally, the reconstruction performance of the FCM algorithm is enhanced. Overall, we show that the enhanced version of the degranulation process is beneficial to reduce the deterioration of the reconstruction results and improve the performance of the mechanism of granulation–degranulation, which is also meaningful for transforming data between numeric form and granular format. We offer a thorough analysis of the developed scheme. In particular, we show that the classical FCM algorithm forms a special case of the proposed scheme. Experiments completed for both synthetic and publicly available datasets demonstrate that the proposed approach outperforms the generic data reconstruction approach.

Cobalt-free perovskites, Ln 0.5 Sr 0·5 Fe 0·8 Cu 0·2 O 3−δ (Ln = La, Pr, and Nd), are systematically evaluated as the cathode materials for intermediate-temperature solid oxide fuel cells (IT-SOFCs) using Gd 0.1 Ce 0·9 O 1.95 as the electrolyte. The samples exhibit an orthorhombic perovskite structures, and their cell volumes decrease as the ionic radius of Ln decreases. Both the oxygen vacancy content and the magnitude of lattice oxygen release per formula unit increase in the temperature range from 370 °C to 850 °C as the ionic radius of Ln decreases. Ln 0.5 Sr 0.5 Fe 0.8 Cu 0.2 O 3−δ is chemically and thermally compatible with the Gd 0.1 Ce 0·9 O 1.95 electrolyte. In the temperature range of 600 °C-750 °C, Nd 0.5 Sr 0·5 Fe 0·8 Cu 0·2 O 3−δ possesses the highest catalytic activity for the oxygen reduction reaction with area specific resistance values of 0.071 Ω cm 2 and 0.141 Ω cm 2 at 750 °C and 700 °C, respectively. The maximum power densities of the anode-supported single cells at 800 °C and 700 °C are 1003.7 mW cm −2 and 516.7 mW cm −2 for Pr 0.5 Sr 0·5 Fe 0·8 Cu 0·2 O 3−δ and 944.5 mW cm −2 and 530.2 mW cm −2 for Nd 0.5 Sr 0·5 Fe 0·8 Cu 0·2 O 3−δ , respectively. Ln 0.5 Sr 0.5 Fe 0.8 Cu 0.2 O 3−δ is shown to be a promising cathode material for IT-SOFCs.

The traditional 2D MUSIC algorithm fixes the azimuth or the elevation, and searches for the other without considering the directions of sources. A spectrum peak diffusion effect phenomenon is observed and may be utilized to detect the approximate directions of sources. Accordingly, a fast 2D MUSIC algorithm, which performs azimuth and elevation simultaneous searches (henceforth referred to as AESS) based on only three rounds of search is proposed. Firstly, AESS searches along a circle to detect the approximate source directions. Then, a subsequent search is launched along several straight lines based on these approximate directions. Finally, the 2D Direction of Arrival (DOA) of each source is derived by searching on several small concentric circles. Unlike the 2D MUSIC algorithm, AESS does not fix any azimuth and elevation parameters. Instead, the adjacent point of each search possesses different azimuth and elevation, i.e., azimuth and elevation are simultaneously searched to ensure that the search path is minimized, and hence the total spectral search over the angular field of view is avoided. Simulation results demonstrate the performance characters of the proposed AESS over some existing algorithms.

During recent years, the development of mechanically robust epoxy resins from natural abundant plant oils is gaining more and more attention. Herein, two strategies were developed to precisely tune the microstructure of plant oil-derived epoxy polymers and mechanical properties of their thermosets. In strategy 1, plant oil-derived epoxy polymers (EPs) with varied densities of epoxy groups were prepared and epoxy resins with tunable mechanical properties were obtained. Tensile test revealed that the mechanical strength, Young's modulus, and toughness increased with the increase of epoxy group density. In strategy 2, the mechanical performanceof plant oil-derived epoxy resins were greatly improved by introducing vanillin as rigid segments. Moreover, the mechanical properties of plant oil-based epoxy resins could be precisely tuned via either strategy 1 or strategy 2, and thermosets from soft elastomers to rigid plastics were obtained. Due to the existence of well-defined chemical networks, plant oil-based epoxy resins exhibit excellent thermally induced shape memory properties.