A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

聚酯帘线的发展趋势

从聚酯的结构设计、帘线浸胶工艺和在轮胎中的应用3个方面阐述聚酯结构特点与产品性能的关系。聚酯帘线行业应该密切关注汽车行业的发展态势,把握市场先机,进一步发展壮大;在新能源汽车逆势增长的形势下,高性能聚酯帘线需求量较大,帘线企业应拓展高性能聚酯帘线在轮胎冠带层、带束层和胎体等部件中的应用,同时联合轮胎企业开发特种聚酯材料和混纺材料等新产品。     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Fabrication of 3D structures via direct ink writing of kaolin/graphene oxide composite suspensions at ambient temperature

Kaolin/graphene oxide composite has been widely utilized in aero-space and architectural engineering applications due to its excellent mechanical property. Direct ink writing (DIW) is a freeform rapid prototyping technology that could be used to accurately fabricate the resulting size with complex shapes. In this study, we reported the DIW of kaolin/graphene oxide (GO) composite suspensions (KGCS) to assemble 3D structures at ambient temperature for the first time. The effects of GO on the chemical constitution and microstructure of kaolin suspensions were investigated. Rheology was characterized to ensure printability of KGCS. The addition of GO in kaolin suspensions quickened a flocculation structure, which dramatically changed their rheology properties. The DIW of 3D structures from the optimal KGCS sample maintained their initial shape without spreading. The flexural and compressive strengths of the dried optimal KGCS samples were obviously enhanced due to the improvement and reduction of the micro-defects compared from cured kaolin matrix.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

Unique CNTs-chained Li4Ti5O12 nanoparticles as excellent high rate anode materials for Li-ion capacitors

Composite anode materials with a unique architecture of carbon nanotubes (CNTs)-chained spinel lithium titanate (Li₄Ti₅O₁₂, LTO) nanoparticles are prepared for lithium ion capacitors (LICs). The CNTs networks derived from commercial conductive slurry not only bring out a steric hindrance effect to restrict the growth of Li₄Ti₅O₁₂ particles but greatly enhance the electronic conductivity of the CNTs/LTO composites, both have contributed to the excellent rate capability and cycle stability. The capacity retention at 30 C (1 C = 175 mA g^?1) is as high as 89.7% of that at 0.2 C with a CNTs content of 11 wt%. Meanwhile, there is not any capacity degradation after 500 cycles at 5 C. The LIC assembled with activated carbon (AC) cathode and such a CNTs/LTO composite anode displays excellent energy storage properties, including a high energy density of 35 Wh kg^?1 at 7434 W kg^?1, and a high capacity retention of 87.8% after 2200 cycles at 1 A g^?1. These electrochemical performances outperform the reported data achieved on other LTO anode-based LICs. Considering the facile and scalable preparation process proposed herein, the CNTs/LTO composites can be very potential anode materials for hybrid capacitors towards high power-energy outputs.     

基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

基于GIS单线图的配网停电单模拟操作应用研究

配电网停电会造成电力系统供配电可靠性以及服务质量下降,研究基于地理信息系统(GIS)单线图的配网停电单模拟操作应用。利用网格长度作为基本单位建立坐标系,以选取起始点与终止点为基础,通过四参数法将GIS坐标映射至图纸网格内,实现配网内设备初步布局,将杆塔、站房和整体均匀分布作为优化目标,设置多目标优化目标函数实现GIS单线图最终优化。选取某电力公司配网作为单模拟操作应用对象,模拟结果表明,单模拟操作配网停电后,该配网各负荷点年故障率、次平均停电时间以及年停电时间均有所减少,可有效提升配网的供配电可靠性。