Tetragonal tungsten bronze phase potential in increasing the piezoelectricity of sol-gel synthesized (K0.5Na0.5)1-xLixNbO3 ceramics

(K,Na)NbO₃ (KNN)-based ceramics have been proven to be formidable candidates among lead-free piezoelectric materials, yet poor reproducibility always hinders their progress. In the present study, the effects of low lithium substitution on the electrical properties and microstructure of (K_0.5Na_0.5)_1-xLi_xNbO₃ (KNLN) ceramics were investigated. All samples were synthesized by the sol-gel method. The Curie temperature (T_C) of the ceramics shifted to higher temperature and gradually decreased the monoclinic-tetragonal (T_M-T) phase transition. Li⁺ substitution had a prominent effect on the ferroelectric properties and improved the piezoelectric coefficient (d₃₃) up to 181 pC/N. X-Ray Diffraction (XRD) studies and Field Emission Scanning Electron Microscopy (FESEM) images revealed an inevitable tetragonal tungsten bronze (TTB) secondary phase, which was formed during the preparation process. It was demonstrated that the volatilization of Li⁺ cations facilitated TTB growth. The coexistence of two different phase structures proved to enhance the KNN piezoelectric performance.     

Crystal structure,polarization properties and reverse nonlinearity of solid solutions of the KNN-PZT system in a wide range of external influences

Experimental research of the crystal structure, polarization properties, and reverse nonlinearity of ceramic solid solutions of the (1-x) (Na_0·5K_0.5)NbO₃-xPb(Ti_0·5Zr_0.5)O₃ (KNN-PZT) quasi-binary system with 0.0 = x ≤ 1.0 in a wide range of external influences (temperatures, strength of dc/ac fields) has been done. Based on the X-ray structural data, an x-T diagram of the system has been constructed, and correlations of the behavior of the macroproperties of solid solutions with the features of their phase states with the temperature change have been established. It has been concluded that it is advisable to use the proposed compositions when designing microelectronic devices operating in various extreme conditions.     

Optimizing the processing conditions of sodium potassium niobate thin films prepared by sol-gel spin coating technique

In the present study, potassium sodium niobate (KNN) thin films were synthesized by means of sol-gel spin coating method. Along with the synthesis, the effects of annealing temperature and various number of coating layers on both the structural and electrical properties were looked into. The results of the study revealed that the annealing temperature had a great impact on the properties of KNN. In addition, the XRD diffractograms and texture coefficient of the synthesized films confirmed that a highly oriented orthorhombic perovskite structure was obtained at 650 °C, whereas at a relatively higher temperature (700 °C), a spurious phase of K₄Nb₆O₁₇ was evolved. In addition, the growth of KNN at 650 °C exhibited a reasonable resistivity value for piezoelectric applications. Looking into the results, it was discovered that the KNN thin films also found to be dependent on a number of coating layers. Field emission scanning electron microscopy (FESEM) showed that KNN with five coating layers was highly crystalline, cracks-free, and had significantly more homogenous surface morphology and the size of grains being uniform, the resistivity of KNN thin films improved with the increasing number of coating layers i.e., up to five.     

基于分位数概要的KNN算法研究

文章简述了分位数概要的相关概念及特点,针对KNN(K最近邻居)的算法特性及应用进行了深入的研究,并在此基础上提出了基于分位数的多值对象的KNN研究问题,为今后的算法研究奠定了基础。     

Synthesis,microstructure and electromechanical properties of undoped (K0·5Na0·5)NbO3

Conventional solid state mixed oxide route using manual and ball milling is investigated for the preparation of K_0·5Na_0·5NbO₃ (KNN) ceramics. Microstructure engineering was made using two milling methods and sintering techniques, and the crystal growth; then electromechanical properties were investigated as a function of sintering temperature, densification and grain size. The sintering conditions were set at 920°C/5 min for spark plasma sintering and 1090–1120°C/10 and 48 h for classical sintering. KNN crystal was grown using floating zone technique under nitrogen gas, where the translation and rotation speeds were fixed at 3 mm h^?1 and 20 rev min^?1 respectively. Piezoelectric and dielectric performances were measured and related to the microstructure. High k_t (33 to 48%), k_p of 18 to 48% and d₃₃ of 127–140 pC N^?1 were reached for relative densities of 84 to 96%. KNN ceramics are now available for the design of ultrasonic sensors.     

大数据相似性连接查询技术研究进展

为了深入理解和全面把握大数据相似性连接查询技术的研究进展，更好地促进其在图片聚类、实体解析、相似文档检测、相似轨迹检索等领域的广泛应用，对大数据相似性连接查询技术相关研究工作进行了深入调研和分析。首先对相似性连接查询的基本概念进行了介绍，然后分别对集合、向量、空间数据、概率数据、字符串等不同类型大数据的相似性连接查询相关研究工作进行了深入研究，对其优缺点进行了分析和总结。最后，指出了大数据相似性连接查询面临的若干挑战性问题及未来的研究重点。     

一种聚类模式下基于密度的改进KNN算法

KNN是基于实例的算法,训练样本的数量影响KNN的分类性能.合理的样本剪裁可以提高分类器的效率.提出了一种聚类条件下基于密度的KNN改进模型.首先使用聚类方法对训练集进行基于类别的选择,裁剪边缘样本以减少噪音;再基于类别密度对样本进行加权,改善k近邻选择时大类别、高密度训练样本的占优现象.试验结果表明,本文提出的改进KNN分类算法提高了KNN的分类效率.     

Microstructure and piezoelectric properties of CuO-doped 0.95(K0.5Na0.5)NbO3-0.05Li(Nb0.5Sb0.5)O3 lead-free ceramics

The effects of sintering temperature and the addition of CuO on the microstructure and piezoelectric properties of 0.95(K_0.5Na_0.5)NbO₃-0.05Li(Nb_0.5Sb_0.5)O₃ were investigated. The KNN-5LNS ceramics doped with CuO were well sintered even at 940 °C. A small amount of Cu²⁺ was incorporated into the KNN-5LNS matrix ceramics and XRD patterns suggested that the Cu²⁺ ion could enter the A or B site of the perovskite unit cell and replace the Nb⁵⁺ or Li⁺ simultaneously. The study also showed that the introduction of CuO effectively reduced the sintering temperature and improved the electrical properties of KNN-5LNS. The high piezoelectric properties of d₃₃ = 263 pC/N, k_p = 0.42, Q_m = 143 and tan δ = 0.024 were obtained from the 0.4 mol% CuO doped KNN-5LNS ceramics sintered at 980 °C for 2 h.     

基于GPU的分类并行算法的研究与实现

分析了KNN算法在GPU上实现并行计算的可能性,提出了通过使用CUDA实现KNN算法的方案,在研究了GPU对存储访问的机制后,通过设计合理的数据以及对算法的改进,避免存储体冲突的产生,提高了算法的健壮性。研究结果证明该方法在GPU上的并行运算速度明显要快于CPU,有着很好的加速比。     

DATA PREPROCESSING AND RE KERNEL CLUSTERING FOR LETTER

Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification （KNN）, Radial Basis Function Neural Network （RBFNN）, and Support Vector Machine （SVM）. Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.