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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.

This study focuses on linguistic information operational realization through information granulation in group decision-making (GDM) scenarios where the preference information offered by decision-makers over alternatives is described using distributed linguistic preference relations (DLPRs). First, an information granulation model is proposed to arrive at the operational realization of linguistic information in the GDM with DLPRs. The information granulation is formulated as a certain optimization problem where a combination of consistency degree of individual DLPRs and consensus degree among individuals is regarded as the underlying performance index. Then, considering that the proposed model is a constrained optimization problem (COP) with an adjustable parameter, which is difficult to be effectively solved using general optimization methods, we develop a novel approach towards achieving the optimal solution, referred to as penalty function-based co-evolutionary particle swarm optimization (PFCPSO). Within the PFCPSO setting, the designed penalty function is used to transform the COPs into unconstrained ones. Besides, the penalty factors and the adjustable parameter, as well as the decision variables of the optimization problems, are simultaneously optimized through the co-evolutionary mechanism of two populations in co-evolutionary particle swarm optimization (CPSO). Finally, a comprehensive evaluation problem about car brands is studied using the proposed model and the newly developed PFCPSO approach, which demonstrates their applicability. Two comparative studies are also conducted to show the effectiveness of the proposals. Overall, this study exhibits two facets of originality: the presentation of the linguistic information granulation model, and the development of the PFCPSO approach for solving the proposed model.

Fuzzy clustering is regarded as an unsupervised learning process that constitutes a prerequisite for many other data mining techniques. Deciding how to classify data efficiently and accurately has been one of the topics pursued by many researchers. We anticipate that the classification performance of the clustering is strongly dependent on the boundary data (viz. data located at the boundaries of the clusters). The boundary data hold some levels of uncertainties and as such contain more information than others. Usually the greater the uncertainty, the more information contained in such data. To improve the quality of clustering, this study develops an augmented scheme of fuzzy clustering, in which a novel weighted data-based fuzzy clustering is proposed. In the introduced scheme, a dataset is composed of boundary data and non-boundary data. The partition matrix is used to determine the boundary data and the non-boundary data to be next considered in the clustering process. Then, we assign different weights to each datum to construct the weighted data. During this process, we make the weights for the boundary data and the non-boundary data different, which makes the contributions of the boundary data and the non-boundary data to the prototypes being reduced and enhanced, respectively. Furthermore, we build a weighting function to determine the weights of the data. The weighted data are used to optimize the prototypes. With the optimized prototypes, the partition matrix can be refined, which ultimately makes the boundaries of the clusters optimized. Finally, the classification performance of fuzzy clustering is enhanced. We offer a thorough analysis of the developed scheme. Comprehensive experimental studies involving synthetic and publicly available datasets are reported to demonstrate the performance of the proposed approach.

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.

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.

The effect of calcium on the properties of SmBa 1–x Ca x CoCuO 5+δ (x = 0.0–0.3) as a cathode material for intermediate-temperature solid oxide fuel cells (IT-SOFCs) is evaluated systematically. Samples exhibit a highly crystalline double perovskite phase, and their cell volumes decrease as x is changed from 0.0 to 0.3. The oxygen content and average thermal expansion coefficient (TEC) of SmBa 1–x Ca x CoCuO 5+δ decreases as the calcium content increased. An average TEC of as low as 15.3 × 10 −6 °C −1 is obtained for SmBa 0.7 Ca 0.3 CoCuO 5+δ . The area specific resistances at 700 °C decrease by approximately 50% when the calcium content is increased from x = 0.0 (0.173 Ω cm 2 ) to x = 0.3 (0.086 Ω cm 2 ). The maximum power densities of SmBa 1−x Ca x CoCuO 5+δ -based single cells at 800 °C increase from 635 mW cm −2 (x = 0.0) to 939 mW cm −2 (x = 0.3).

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.

In this paper, a multi-direction virtual array transformation algorithm termed MVATA for two dimension (2D) direction of arrival (DOA) estimation is proposed. A kind of multi-direction rotation invariant virtual subarrays is firstly constructed. Secondly, by singular value decomposition (SVD) of the response matrix of the real array to signals from a sector, the interpolation matrices can be estimated and from which the real and virtual signal subspaces are further estimated. Thirdly, a rotational invariant factor matrix is derived from the real and virtual subspaces base on least squares. Subsequently, the azimuth and elevation can be solved through the rotational invariant factor matrix without spectral peak search. Theoretical calculation and time-consuming statistics show that the proposed MVATA has the advantage of computational complexity compared with the conventional interpolated methods. Moreover, the proposed MVATA can be applied to arbitrary structure array, and the estimated 2D angles can be paired automatically. Simulation results demonstrate the proposed MVATA possesses significantly accuracy improvement especially in low SNR and small snapshots. Proposed algorithm need not the support of pairing algorithm.Complexity is reduced without spectrum search and fractal dimension processing.Accuracy is obviously improved especially in low SNR and small snapshots.2D angels have the same azimuth or elevation can be distinguished.Proposed can be applied not only to UCA but also to arbitrary structure arrays.

An understanding of the relationships between the processing, compositions, and properties of perovskites is vitally important in the search for high performance cathode materials for intermediate-temperature solid oxide fuel cells (IT-SOFCs). In this work, the properties of PrBa 1−x Co 2 O 5+δ (x = 0.00–0.10) double perovskites were modified by introducing K during synthesis, and their performances as the cathode materials for IT-SOFCs were then characterized. The addition of K resulted in a greater cation deficiency in the double perovskites, improving bulk oxygen transport. However, K addition also markedly decreased the Co ion content on the perovskites' surfaces, which significantly deteriorated the perovskites’ electrochemical performances related to the surface exchange of oxygen.

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.

A validated HPLC-DAD-ESI-MS method has been developed to simultaneously quantify 12 bioactive compounds in the seeds of Notopterygium incisum Ting ex H.T. Chang and Notopterygium franchetii H. de Boiss whose rhizomes and roots are widely used as traditional Chinese medicine. This method was validated to be sensitive, precise and accurate and was applied to evaluate the difference in the chemical profiles and contents of these analytes in 37 batches of N. incisum and 31 batches of N. franchetii samples collected from different locations. Principal component analysis showed that the two species were separated into two groups obviously. This study established a validated method for identification of the authenticity of the seeds of N. incisum and N. franchetii and supplied effective guidance for artificial cultivation.

Two new triterpene saponins, paraquinosides A (1) and B (2) were isolated from the aerial parts of Paraquilegia microphylla (Royle) Dromm. et Hutch, a Tibetan ethnic medicine distributed in the Qinghai-Tibet Plateau. On the basis of 10 and 20 NMR evidence, their structure was elucidated as 3-O-alpha-L-rhamnopyranosyl (1 -> 2)-beta-D-glucopyranosyl-15-dehydroxyl-16-O-methyl-24, 25-deoxy-26-hydroxylshengmanol-26-O-beta-D-glucopyranoside (1) and 3-O-alpha-L-Rhamnopyranosyl (1 -> 2)-[beta-D-glucopyranosyl(1 -> 3)]-beta-D-glucopyranosyl-15-dehydroxyl-16-O-methyl-24, 25-deoxy-26-hydroxylshengmanol-26-O-beta-D-glucopyranoside (2), respectively. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.