Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Influence of Si-Doping on the Performance of InGaN/GaN Multiple Quantum Well Solar Cells

Semiconductors - The performance of InGaN/GaN multiple quantum well (MQW) solar cells with five different Si-doping concentrations, namely 0, 4 × 1017 cm–3, 1 × 1018 cm–3, 3...     

知竹,知足——浅谈紫砂壶“知竹”的文化意境

紫砂壶以造型为基本的载体,通过形神兼备的造型表现出深厚的文化情感,它将自然万物融汇其中,将各种不同的生命、思想和个性呈现其上,使人观壶如同与人交流,产生深刻的情感、精神共鸣,感受到内心的平静,这十分有益于舒缓当代人的精神心理压力,让人们通过泡茶品茗这种生活方式来获得暂时的悠闲,并通过对紫砂壶的把玩来感悟其中的文化内蕴,从而获得自由淡泊的心态,更好地面对生活。本文以紫砂壶“知竹”为例,浅谈其文化意境。     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

In-situ crystal growth and photoluminescence properties of YBO3: Tb3+ microstructures

Three-dimensional (3D) YBO₃:Tb³⁺ flower-like and dense flower-like hierarchitecture constituted of nanoflakes are solvothermally synthesized in the presence of polyborate precursors in the mixture of ethanol and water. The growth process of the YBO₃:Tb³⁺ flowers and dense flowers was explored based on the time-dependent experiment and the results showed that the growth mechanism follows an in situ growth rather than self-assembly process as reported previously. YBO₃:Tb³⁺ morphologies composed of nanoflakes are achieved by controlling the concentration of ethanol and dependence of photoluminescence on morphology was studied. Remarkable photoluminescence enhancement was observed for YBO₃:Tb³⁺ with flower-like morphology demonstrating the potential of the microstructure in future applications as a green phosphor. Such a synthetic method and growth mechanism may be applied to fabricate complex 3D architectures of other materials.     

Structural,magnetic and magnetostrictive properties of Co1-xLixFe2O4 synthesized by cathode materials of spent Li-ion batteries

In this study, we investigated the effects of substituting Li⁺ for Co²⁺ at the B sites of the spinel lattice on the structural, magnetic and magnetostrictive properties of cobalt ferrites. The Li⁺ substituted cobalt ferrites, Co_1-xLi_xFe₂O₄, with x varying from 0 to 0.7 in 0.1 increments, were synthesized with a sol-gel auto-combustion method using the cathode materials of spent Li-ion batteries. X-ray diffraction analysis revealed that all the Co_1-xLi_xFe₂O₄ nanopowders had a single-phase spinel structure and the lattice parameters decreased with increasing Li⁺ content, which can be proved by slight shifts towards higher diffraction angle values of the (311) peak. Field emission scanning electron microscopy was used to observe the fractured inner surface of the sintered cylindrical rods and the increased porosity resulted in a decreased magnetostriction. The oxidation states of Co and Fe in the cobalt ferrite samples were examined by X-ray photoelectron spectroscopy. High resolution transmission electron microscopy micrographs showed that most particles were roughly spherical and with sizes of 25–35?nm. Li⁺ substitution had a strong effect on the saturation magnetization and coercivity, which were characterized with a vibrating sample magnetometer. The Curie temperature was reduced due to the decrease in magnetic cations and the weakening of the exchange interactions. The magnetostrictive properties were influenced by the incorporation of Li⁺ at the B sites of the spinel structure and correlated with the changes in porosity, magnetocrystalline anisotropy and the cation distribution.     

土壤中石油污染物的脱附过程

文章中主要采用有机溶剂萃取法（OSE）对高浓度石油污染土壤进行修复，经过精馏操作，有效回收原油，建立脱附等温曲线，研究了石油污染物在土壤-有机溶剂两相间的迁移规律。